Genetic_Algorithm_Assignment.ipynb

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      "name": "Genetic Algorithm Assignment.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/adhishagc/Genetic-Algorithm-using-Python-Example/blob/master/Genetic_Algorithm_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## **Table of Contents**\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "1.   **Importing Libraries**\n",
        "2.   **Access to Google Drive**\n",
        "3.**Function Definitions**\n",
        "4. **Running the Model**\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BEFLTAgH9Jxo",
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      "source": [
        "### **Initialization**\n",
        "\n",
        "\n",
        "1.   Import of Libraries\n",
        "2.   Access to Google Drive\n",
        "3. Load the Dataset\n",
        "4. View the Dataset as a Dataframe\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-eurb3QNqpfi",
        "colab_type": "code",
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      },
      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random as random\n",
        "from matplotlib import pyplot as plt\n",
        "import time"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbdYNXe5rWwX",
        "colab_type": "code",
        "outputId": "e7a67466-736f-461b-9580-2c2a4da1099e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#Access to google drive. The Video Store dataset was uploaded to google drive.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2x9m61XNt9iD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#The dataset is load dataset to a dataframe\n",
        "df = pd.read_csv('/content/gdrive/My Drive/Genetic Algorithms/Assignment 01/dataset.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfQMTCpC-rop",
        "colab_type": "code",
        "outputId": "2863ffcb-2025-41e7-e7a5-f88608ff9f4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "#view dataset\n",
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>size</th>\n",
              "      <th>duration</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>85</td>\n",
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              "      <td>130</td>\n",
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              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1500</td>\n",
              "      <td>120</td>\n",
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            "text/plain": [
              "   file  size  duration\n",
              "0     1   800       121\n",
              "1     2   700        95\n",
              "2     3   650        85\n",
              "3     4   750       100\n",
              "4     5   600        78\n",
              "5     6   900       125\n",
              "6     7   950       130\n",
              "7     8   875       128\n",
              "8     9  1050       135\n",
              "9    10  1500       120"
            ]
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      "source": [
        "### **Population Creation Function**\n",
        "A dataframe which consists of the entire population is returned when this function is called. The structure would be of 1 column to label the chromosome number and the remaining 10 columns will represent the file number of the dataset. The returned dataframe (population) will consist of random 0 or 1 values in the  chromosome."
      ]
    },
    {
      "cell_type": "code",
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        "id": "JKhFpNlW6UCH",
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      "source": [
        "# function for init population\n",
        "def createInitPop(popSize):\n",
        "  #array of data to represent the chromosomes and random file labellings\n",
        "  values ={\n",
        "    'chromosome':[i for i in range(1,popSize+1)],\n",
        "    'file 1':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 2':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 3':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 4':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 5':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 6':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 7':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 8':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 9':[random.randint(0, 1) for _ in range(popSize)],\n",
        "    'file 10':[random.randint(0, 1) for _ in range(popSize)],\n",
        "  }\n",
        "  #convert the array of population to a dataframe\n",
        "  values = pd.DataFrame(values)\n",
        "  \n",
        "  #return the dataframe\n",
        "  return values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o45jFGcl_Rq_",
        "colab_type": "text"
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      "source": [
        "### **Evaluate Fitness Function**\n",
        "\n",
        "\\begin{equation}\n",
        "f(x_i) = \\textrm{Playing time of video file of } x_i\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "g(x_i) = \\textrm{File size of video file of } x_i\n",
        "\\end{equation}\n",
        "\n",
        "**Objective Function**\n",
        "\\begin{equation}\n",
        "\\textrm{Maximize }\\sum f(x_i) \\textrm{ such that } \\sum g(x_i)<=4500\n",
        "\\end{equation}"
      ]
    },
    {
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      "source": [
        "#Evaluate Fitness Function\n",
        "def EvaluateFitness(chromosome,df,popSize):\n",
        "  #fitness array\n",
        "  fitness = {\n",
        "      'chromosome':[x for x in range(1,popSize+1)],\n",
        "      'fitness':[0 for x in range(0,popSize)]\n",
        "  }\n",
        "  \n",
        "  #size array\n",
        "  filesize = {\n",
        "      'chromosome':[x for x in range(1,popSize+1)],\n",
        "      'size':[0 for x in range(0,popSize)]\n",
        "  }\n",
        "\n",
        "  #convert to dataframe\n",
        "  fitness = pd.DataFrame(fitness)\n",
        "  filesize = pd.DataFrame(filesize)\n",
        "\n",
        "  #total file size and duration PER Solution\n",
        "  size=0\n",
        "  duration=0\n",
        "  \n",
        "  #implementation of the Fitness Function\n",
        "  # i navigates the rows while the j navigates the file1, file2 ... columns in the chromosome.\n",
        "  for i in range(popSize):\n",
        "    for j in range(1,11):\n",
        "      #navigate through each chromosome in the population and check for the cells which contains a file, that is 1 value in the cell.\n",
        "      \n",
        "      if(chromosome.iloc[i,j] ==1):\n",
        "        #if it contains any file in the cell(that is a 1 value) the size and the duration of the solution is calculated by comparing the dataset.\n",
        "        size += df.iloc[j-1,1]\n",
        "        duration +=df.iloc[j-1,2]\n",
        "    \n",
        "    #calculation of the fitness and the total file size of the entire population\n",
        "    if(size<=4500):\n",
        "      #these values are potential solutions since the size is less than 4500.\n",
        "      #respective fitness and filesizes are added to the dataframe\n",
        "      fitness.loc[i,'fitness'] = duration\n",
        "      filesize.loc[i,'size'] = size\n",
        "      \n",
        "    else:\n",
        "      #These will be non solutions. Therefore the fitness is reduced by multiplying by 0.5\n",
        "      #The file size is stored\n",
        "      fitness.loc[i,'fitness'] = duration*0.5\n",
        "      filesize.loc[i,'size'] = size\n",
        "      \n",
        "    #The PER solution size and duration values are reset\n",
        "    size = 0\n",
        "    duration = 0\n",
        "    #Debug_fitness = fitness\n",
        "  #The entire fitness and filesize of the population is returned as a DataFrame\n",
        "  return fitness, filesize      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1iNbgvw07dr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Debug_fitness = pd.DataFrame()"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
        "id": "HkVXXOm-1hjH",
        "colab_type": "code",
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      "source": [
        "popSize = 60 \n",
        "\n",
        "#creation of population of Chromosomes\n",
        "chromosome = createInitPop(popSize)\n",
        "fitness, totFileSize = EvaluateFitness(chromosome,df,popSize)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "cell_type": "code",
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        "id": "uCIAaEQL15Da",
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      "source": [
        "fitness"
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      "execution_count": 9,
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>216.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>293.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>451.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>350.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>382.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>215.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>367.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>503.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>285.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>461.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>428.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>503.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>322.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>585.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>491.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>425.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>478.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>329.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>405.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>348.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>318.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>310.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>362.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>461.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>455.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>269.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>306.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>466.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>375.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>32</td>\n",
              "      <td>329.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>33</td>\n",
              "      <td>528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>316.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>329.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>36</td>\n",
              "      <td>344.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>37</td>\n",
              "      <td>374.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>38</td>\n",
              "      <td>223.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>39</td>\n",
              "      <td>531.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>40</td>\n",
              "      <td>556.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>41</td>\n",
              "      <td>319.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>42</td>\n",
              "      <td>391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>43</td>\n",
              "      <td>401.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>44</td>\n",
              "      <td>448.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>45</td>\n",
              "      <td>571.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>46</td>\n",
              "      <td>468.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>47</td>\n",
              "      <td>347.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>48</td>\n",
              "      <td>448.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>49</td>\n",
              "      <td>385.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>50</td>\n",
              "      <td>436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>51</td>\n",
              "      <td>390.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>52</td>\n",
              "      <td>360.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>53</td>\n",
              "      <td>315.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>54</td>\n",
              "      <td>455.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>55</td>\n",
              "      <td>493.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>56</td>\n",
              "      <td>443.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>57</td>\n",
              "      <td>298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>58</td>\n",
              "      <td>401.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>59</td>\n",
              "      <td>274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>60</td>\n",
              "      <td>289.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    chromosome  fitness\n",
              "0            1    216.0\n",
              "1            2    293.0\n",
              "2            3    451.0\n",
              "3            4    350.0\n",
              "4            5    382.0\n",
              "5            6    215.0\n",
              "6            7    367.0\n",
              "7            8    274.0\n",
              "8            9    503.0\n",
              "9           10    285.0\n",
              "10          11    461.0\n",
              "11          12    428.5\n",
              "12          13    503.0\n",
              "13          14    322.0\n",
              "14          15    585.0\n",
              "15          16    491.0\n",
              "16          17    425.0\n",
              "17          18    478.0\n",
              "18          19    337.0\n",
              "19          20    329.0\n",
              "20          21    405.0\n",
              "21          22    348.0\n",
              "22          23    318.5\n",
              "23          24    310.5\n",
              "24          25    362.0\n",
              "25          26    461.0\n",
              "26          27    455.5\n",
              "27          28    269.0\n",
              "28          29    306.5\n",
              "29          30    466.0\n",
              "30          31    375.5\n",
              "31          32    329.0\n",
              "32          33    528.0\n",
              "33          34    316.0\n",
              "34          35    329.5\n",
              "35          36    344.5\n",
              "36          37    374.0\n",
              "37          38    223.0\n",
              "38          39    531.0\n",
              "39          40    556.0\n",
              "40          41    319.5\n",
              "41          42    391.0\n",
              "42          43    401.0\n",
              "43          44    448.5\n",
              "44          45    571.0\n",
              "45          46    468.0\n",
              "46          47    347.0\n",
              "47          48    448.0\n",
              "48          49    385.5\n",
              "49          50    436.0\n",
              "50          51    390.5\n",
              "51          52    360.0\n",
              "52          53    315.0\n",
              "53          54    455.5\n",
              "54          55    493.5\n",
              "55          56    443.0\n",
              "56          57    298.0\n",
              "57          58    401.0\n",
              "58          59    274.0\n",
              "59          60    289.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ta_rJ50k1lMl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fitness_id = fitness[['fitness']].idxmax()\n",
        "highest_fitness = fitness.iloc[fitness_id[0]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bNPNbRJ1yxh",
        "colab_type": "code",
        "outputId": "a3da1f2d-7f43-43aa-dcc5-648c0f4d7656",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "highest_fitness[1]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "585.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDNuThva_Wj-",
        "colab_type": "text"
      },
      "source": [
        "### **Rank Selection Function**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHnFx9w2g8wL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Rank Selection Function\n",
        "def rankSelection(chromosome,fitness,popSize):\n",
        "  #Total integer value from 0 to population size \n",
        "  #rankTotal = sum(x for x in range(popSize))\n",
        "  rankTotal = 0\n",
        "  for x in range(popSize):\n",
        "    rankTotal += x\n",
        "    \n",
        "  #A random value inbetween 0 and above value\n",
        "  totRandom = random.uniform(0,1)*rankTotal\n",
        "  \n",
        "  #variable to store the accumilation of fitness values\n",
        "  tot=0\n",
        "  #sorting the fitness dataframe having the lowest on the top and the highest in the bottom\n",
        "  fitness_sorted = fitness.sort_values(by=['fitness'])\n",
        "  \n",
        "  for i in fitness_sorted.index:\n",
        "    #accumilation of the fitness values till the accumilation is equal to the random total generated\n",
        "    tot += fitness_sorted.loc[i,'fitness']\n",
        "    if(tot>=totRandom):\n",
        "      global fifo_index\n",
        "      fifo_index = i\n",
        "      \n",
        "      #returning the chromosome(as a parent selected) and the respective value i where the accumilation stopped. \n",
        "      selected_parent = chromosome.loc[i,:]\n",
        "      selected_parent_id = i\n",
        "      \n",
        "      return selected_parent,selected_parent_id"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eToBCVTf5gme",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Roulette Wheel Function\n",
        "def Roulette_wheel(chromosome,fitness,popSize):\n",
        "  #Total integer value from 0 to population size \n",
        "  #rankTotal = sum(x for x in range(popSize))\n",
        "  #rankTotal = 0\n",
        "  sum_of_fitness = 0\n",
        "  for i in range(popSize):\n",
        "    sum_of_fitness += fitness.loc[i,'fitness']\n",
        "    \n",
        "  #A random value inbetween 0 and above value\n",
        "  totRandom = random.uniform(0,sum_of_fitness)\n",
        "  \n",
        "  #variable to store the accumilation of fitness values\n",
        "  tot=0\n",
        "  #sorting the fitness dataframe having the lowest on the top and the highest in the bottom\n",
        "  #fitness_sorted = fitness.sort_values(by=['fitness'])\n",
        "  \n",
        "  for i in fitness.index:\n",
        "    #accumilation of the fitness values till the accumilation is equal to the random total generated\n",
        "    tot += fitness.loc[i,'fitness']\n",
        "    if(tot>=totRandom):\n",
        "      global fifo_index\n",
        "      fifo_index = i\n",
        "      \n",
        "      #returning the chromosome(as a parent selected) and the respective value i where the accumilation stopped. \n",
        "      selected_parent = chromosome.loc[i,:]\n",
        "      selected_parent_id = i\n",
        "      \n",
        "      return selected_parent,selected_parent_id"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OivP39RFRSVS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tournament_selection(chromosome,fitness,popSize):\n",
        "  #count the loop\n",
        "  count = 1\n",
        "  #needed offspring count\n",
        "  offspring_needed = 1\n",
        "  \n",
        "  \n",
        "  #NEEDs modification. This function should be able to hold needed number of parents. But only for the questions purpose \n",
        "  #only 1 parent is selected\n",
        "  selected_parent = pd.DataFrame()\n",
        "  selected_parent_id = -1\n",
        "\n",
        "  while(count <= offspring_needed):\n",
        "    rand_id_1 = random.randint(0,popSize)\n",
        "    rand_id_2 = random.randint(0,popSize)\n",
        "    \n",
        "    if(fitness.loc[rand_id_1,'fitness'] > fitness.loc[rand_id_2,'fitness']):\n",
        "      selected_parent = chromosome.loc[rand_id_1,:]\n",
        "      selected_parent_id = rand_id_1\n",
        "      count += 1\n",
        "      \n",
        "    \n",
        "    elif (fitness.loc[rand_id_1,'fitness'] < fitness.loc[rand_id_2,'fitness']):\n",
        "      selected_parent = chromosome.loc[rand_id_2,:]\n",
        "      selected_parent_id = rand_id_2\n",
        "      count += 1\n",
        "  \n",
        "  return selected_parent,selected_parent_id\n",
        "  \n",
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrydFJLYZ8TN",
        "colab_type": "text"
      },
      "source": [
        "### **Binary Represented N Point Crossover Function Generalized**\n",
        "The function has generalized the Single Point Crossover and the N-Point Crossover operators.\n",
        "When :\n",
        "\\begin{equation}\n",
        "n = 1...n \\textrm{ which is the n-point}\n",
        "\\end{equation}\n",
        "is passed as a parameter it the function it acts as a single point crossover and for the rest as an n point crossover. Also the :\n",
        "\\begin{equation}\n",
        "p_c = 0.8 \\textrm{ or etc, crossover rate}\n",
        "\\end{equation}\n",
        "would be the crossover rate for the function.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVeLCuF3Z6jR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def binary_rep_n_point_crossover(n,parent1,parent2,pc):\n",
        "  \n",
        "  parent1_parts = [] #this list contains parent 1 partwise split sections based on the random values generated\n",
        "  parent2_parts = [] #this list contains parent 2 partwise split sections based on the random values generated\n",
        "  random_vals = [] #list to store the generated random values\n",
        "  start_pos = 0 #start position of the spiliting parents\n",
        "  random_position = start_pos+1 #initial random position\n",
        "  L_1 = 10-1 # the random value range should be in between 1 to 10-1 which is L-1\n",
        "\n",
        "  parent1_array = parent1.values\n",
        "  parent2_array = parent2.values\n",
        "\n",
        "  if(random.uniform(0,1)>=pc):\n",
        "    #crossover takes place\n",
        "    count = 1\n",
        "\n",
        "    for n_times in range(n+1):\n",
        "      #random position is generated here. This random position will be the place\n",
        "      #where the chromosome will be split\n",
        "      random_position = random.randint(random_position,L_1)\n",
        "\n",
        "      #The generated random values are stored in this list\n",
        "      random_vals.append(random_position)\n",
        "\n",
        "\n",
        "      if(n_times == n):\n",
        "        parent1_parts.append(parent1_array[start_pos:L_1+1])\n",
        "        parent2_parts.append(parent2_array[start_pos:L_1+1])\n",
        "\n",
        "      else:\n",
        "\n",
        "        parent1_parts.append(parent1_array[start_pos:random_position])\n",
        "        parent2_parts.append(parent2_array[start_pos:random_position])\n",
        "\n",
        "      start_pos = random_position\n",
        "      #random_position +=1\n",
        "\n",
        "\n",
        "    for i in range(len(parent1_parts)):\n",
        "      if(count%2==0):\n",
        "        temp = parent1_parts[i]\n",
        "        parent1_parts[i] = parent2_parts[i]\n",
        "        parent2_parts[i] = temp\n",
        "        count +=1\n",
        "      else:\n",
        "        #nothing\n",
        "        count += 1\n",
        "\n",
        "\n",
        "    offspring1_array = np.concatenate(([parent1_parts[i] for i in range(len(parent1_parts))]))\n",
        "    offspring2_array = np.concatenate(([parent2_parts[i] for i in range(len(parent1_parts))]))\n",
        "    return offspring1_array,offspring2_array\n",
        "  \n",
        "  else:\n",
        "    return parent1_array,parent2_array \n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lADMbtA3sG5V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def uniform_crossover(n,parent1,parent2,p):\n",
        "  \n",
        "  parent1_parts = [] #this list contains parent 1 partwise split sections based on the random values generated\n",
        "  parent2_parts = [] #this list contains parent 2 partwise split sections based on the random values generated\n",
        "  random_vals = [] #list to store the generated random values\n",
        "  start_pos = 0 #start position of the spiliting parents\n",
        "  random_position = start_pos+1 #initial random position\n",
        "  L_1 = 10-1 # the random value range should be in between 1 to 10-1 which is L-1\n",
        "\n",
        "  parent1_array = parent1.values\n",
        "  parent2_array = parent2.values\n",
        "  offspring1_array = np.empty([len(parent1_array)])\n",
        "  offspring2_array = np.empty([len(parent1_array)])\n",
        "  \n",
        "  #create random array\n",
        "  rand_array = np.array([random.randint(0, 1) for _ in range(len(parent1_array))])\n",
        "  \n",
        "  for i in range(len(parent1_array)):\n",
        "    if(rand_array[i] < p):\n",
        "      #inherit from 1st parent\n",
        "      offspring1_array[i] = parent1_array[i]\n",
        "      offspring2_array[i] = parent2_array[i]\n",
        "    else :\n",
        "      #inherit from 2nd parent\n",
        "      offspring1_array[i] = parent2_array[i]\n",
        "      offspring2_array[i] = parent1_array[i]\n",
        "  \n",
        "  return offspring1_array,offspring2_array"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYDi-eCZ_hVG",
        "colab_type": "text"
      },
      "source": [
        "### **BitFlip of the Mutation**\n",
        "Bit flip is done based on a randomly generated array of numbers compared with the mutation rate variable. If the compared value is higher than the mutation rate bit value is flipped.\n",
        "\\begin{equation}\n",
        "p_m = 0 .... 1  \\textrm{ Mutation rate Variable}\n",
        "\\end{equation}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnNbniZ-LbQi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def bitflip(chromosome,pm):\n",
        "  #generate random bit array\n",
        "  bits = [random.uniform(0,1) for _ in range(10)] #range size is the number of files\n",
        "  \n",
        "  for i in range(10):\n",
        "    if(bits[i]>=pm):\n",
        "      if(chromosome[i] == 0):\n",
        "        chromosome[i] = 1\n",
        "      else:\n",
        "        chromosome[i] = 0\n",
        "  \n",
        "  return chromosome\n",
        "      "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtPy1zlX_dY_",
        "colab_type": "text"
      },
      "source": [
        "### **Supportive Functions**\n",
        "These functions are used for retrieving individual chromosome information. Much used for debugging purposes.\n",
        "\n",
        "1.   **getFitness** function retruns the fitness and the total size of the Chromosome\n",
        "2.   **getDetails** function returns the total file size and the total duration of the Chromosome\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TXQpT10lC5N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getFitness(chromosome):\n",
        "  total_size = 0\n",
        "  total_duration = 0\n",
        "  \n",
        "  for i in range(len(chromosome)):\n",
        "    if(chromosome[i]==1):\n",
        "      total_size += df.loc[i,'size']\n",
        "      total_duration += df.loc[i,'duration']\n",
        "  \n",
        "  if(total_size>4500):\n",
        "    fitness = total_duration*0.5\n",
        "    return fitness,total_size\n",
        "  else:\n",
        "    fitness = total_duration\n",
        "    \n",
        "    return fitness,total_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Mni32HhOggn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getFitness_for_runs(chromosome):\n",
        "  total_size = 0\n",
        "  total_duration = 0\n",
        "  \n",
        "  for i in range(len(chromosome)):\n",
        "    if(chromosome[i]==1):\n",
        "      total_size += df.loc[i,'size']\n",
        "      total_duration += df.loc[i,'duration']\n",
        "  \n",
        "  if(total_size>4500):\n",
        "    fitness = total_duration*0.5\n",
        "    return fitness,total_duration,total_size\n",
        "  else:\n",
        "    fitness = total_duration\n",
        "    \n",
        "    return fitness,total_duration,total_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npW9BN7ppNiz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDetails(chromosome):\n",
        "  total_size = 0\n",
        "  total_duration = 0\n",
        "  \n",
        "  for i in range(len(chromosome)):\n",
        "    if(chromosome[i]==1):\n",
        "      total_size += df.loc[i,'size']\n",
        "      total_duration += df.loc[i,'duration']\n",
        "      \n",
        "  return total_duration, total_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZbApi5tApCR",
        "colab_type": "text"
      },
      "source": [
        "### **Replace the Offspring with the Population**\n",
        "Offspring is replaced based on age. That is the selected parents are replaced.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7RbOz8pQZhZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def replace_with_offspring(parent1_id,parent2_id,offspring1,offspring2):\n",
        "  #Offspring is replaced based on age. That is the selected parents are replaced.\n",
        "  chromosome.iloc[parent1_id,1:] = offspring1\n",
        "  chromosome.iloc[parent2_id,1:] = offspring2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElR3n9xN_lNP",
        "colab_type": "text"
      },
      "source": [
        "### **Iterate the Model**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slw5Q3PfSaUe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def trainModel(n,n_point,p_c,p_m):\n",
        "  #variable to count and display the current epoch\n",
        "  epoch = 0\n",
        "  fit = pd.DataFrame()\n",
        "  filesize = pd.DataFrame()\n",
        "  parent1 = pd.DataFrame()\n",
        "  parent2 = pd.DataFrame()\n",
        "  \n",
        "  p = 0.5 #this is used only in uniform crossover\n",
        "  \n",
        "  for i in range(n):\n",
        "    #Fitness Evaluation\n",
        "    \n",
        "    fitness, totFileSize = EvaluateFitness(chromosome,df,popSize)\n",
        "    \n",
        "    #parent selection\n",
        "    parent1, parent1_id = rankSelection(chromosome,fitness,popSize)\n",
        "    parent2, parent2_id = rankSelection(chromosome,fitness,popSize)\n",
        "    \n",
        "    # Roulette_wheel Selection\n",
        "    #parent1, parent1_id = Roulette_wheel(chromosome,fitness,popSize)\n",
        "    #parent2, parent2_id = Roulette_wheel(chromosome,fitness,popSize)\n",
        "    \n",
        "    #tournament selection\n",
        "    #parent1, parent1_id = tournament_selection(chromosome,fitness,popSize)\n",
        "    #parent2, parent2_id = tournament_selection(chromosome,fitness,popSize)\n",
        "    \n",
        "    #offspring generation through crossover\n",
        "    offspring1,offspring2 = binary_rep_n_point_crossover(n_point,parent1[1:],parent2[1:],p_c)\n",
        "    \n",
        "    \n",
        "    #offspring1,offspring2 = uniform_crossover(n_point,parent1[1:],parent2[1:],p)\n",
        "    \n",
        "    #passing the generated offspring for mutation\n",
        "    offspring1 = bitflip(offspring1,p_m)\n",
        "    offspring2 = bitflip(offspring2,p_m)\n",
        "    \n",
        "    #Using Elitism with FIFO\n",
        "    \n",
        "    #Getting the current fittest in the population\n",
        "    #fitness_only = fitness[['fitness']]\n",
        "    fitness_id = fitness[['fitness']].idxmax()\n",
        "    highest_fitness = fitness.iloc[fitness_id[0]]\n",
        "    #get fitness of generated offspring\n",
        "    fitness_offspring_1,size = getFitness(offspring1)\n",
        "    fitness_offspring_2,size = getFitness(offspring2)\n",
        "    \n",
        "    if(highest_fitness[1] > fitness_offspring_1):\n",
        "     offspring1 = chromosome.iloc[fitness_id[0],1:].values\n",
        "     #print(1)\n",
        "    elif (highest_fitness[1] > fitness_offspring_2):\n",
        "     offspring2 = chromosome.iloc[fitness_id[0],1:].values\n",
        "     #print(2)\n",
        "    \n",
        "    \n",
        "    # UNCOMMENT above if part to avoid Elitism replace parents\n",
        "    replace_with_offspring(parent1_id,parent2_id,offspring1,offspring2)\n",
        "    \n",
        "    #display epoch\n",
        "    #print(epoch)\n",
        "    #epoch +=1\n",
        "    \n",
        "    #debugging purposes returning values\n",
        "    fit = fitness\n",
        "    filesize = totFileSize\n",
        "    \n",
        "    #return parent1,parent2,offspring1,offspring2\n",
        "  #return fit,filesize,parent1,parent2     # general purpose\n",
        "  #return \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcjScGui4ERn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "abc = np.array([1,2,3,4,5,6,7,8,9,10])\n",
        "xxx = chromosome.iloc[39,1:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUvdz4lC4J4d",
        "colab_type": "code",
        "outputId": "65e59d14-af93-4f5b-e3d0-53eafb0d55ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "abc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbeTKXW-4tw9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "abc = xxx.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rTczSl4w4v4j",
        "colab_type": "code",
        "outputId": "bb4bf2a1-8280-4260-9588-02ed3907d192",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "abc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 0, 1, 1, 1, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsu7YB2Sw0Bw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df3 = pd.DataFrame({\"A\":[4, 5, 2, 6],  \n",
        "                   \"B\":[11, 2, 5, 8], \n",
        "                   \"C\":[1, 8, 66, 4]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJWwbrd9zPKr",
        "colab_type": "code",
        "outputId": "a9676c3a-85d4-44bc-d011-e6a2810f88ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df3"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   A   B   C\n",
              "0  4  11   1\n",
              "1  5   2   8\n",
              "2  2   5  66\n",
              "3  6   8   4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WNf2BFULzRmp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df4 = df3[['A']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNB0wzToznaj",
        "colab_type": "code",
        "outputId": "bb564cf7-d67c-477c-8252-1492a0d48fd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "df4.idxmax(axis=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGAXyV85_p6m",
        "colab_type": "text"
      },
      "source": [
        "### **Run the Program**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3sAEJ26EO3X",
        "colab_type": "code",
        "outputId": "80260bdf-2912-41b0-a644-0201cf00d080",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#SCENE 01\n",
        "#population size\n",
        "popSize = 50 \n",
        "\n",
        "#creation of population of Chromosomes\n",
        "chromosome = createInitPop(popSize)\n",
        "\n",
        "#fifo index relates to the index to be removed. -1 is the initial value\n",
        "fifo_index = -1\n",
        "\n",
        "#iteration and \n",
        "iterations = 100\n",
        "\n",
        "#n point for crossover\n",
        "n_point = 1\n",
        "#crossover rate\n",
        "p_c = 0.8\n",
        "#mutation rate\n",
        "p_m = 0.01\n",
        "\n",
        "#Final population Duration and Size storage\n",
        "finalFitness = []\n",
        "finalDuration = []\n",
        "finalSize = []\n",
        "\n",
        "\n",
        "#train model\n",
        "start = time.time()\n",
        "#fit,filesize,parent1,parent2 = trainModel(iterations,n_point,p_c,p_m)\n",
        "trainModel(iterations,n_point,p_c,p_m)\n",
        "end = time.time()\n",
        "print(\"Population Size :\" + str(popSize))\n",
        "print(\"Iterations :\" + str(iterations))\n",
        "print(\"Elapsed time :\" + str(end - start) + \" seconds\")\n",
        "\n",
        "#Get Fitness and Size information for the Last Population\n",
        "for i in range(popSize):\n",
        "  fitness,duration,size = getFitness_for_runs(chromosome.iloc[i,1:].values)\n",
        "  finalDuration.append(duration)\n",
        "  finalFitness.append(fitness)\n",
        "  finalSize.append(size)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Population Size :50\n",
            "Iterations :100\n",
            "Elapsed time :4.806628704071045 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUU2lBGpww7U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Getting the Optimum solution Information to Variables\n",
        "Best_Solution_id = finalFitness.index(max(finalFitness))\n",
        "Best_Solution_Size = finalSize[Best_Solution_id] \n",
        "Best_Solution_Duration = finalDuration[Best_Solution_id]\n",
        "Best_Solution = chromosome.iloc[Best_Solution_id]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcD2gdPVozvl",
        "colab_type": "code",
        "outputId": "e81d6c68-dcdb-4e3d-e601-1c3febbdb1dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "#Printing the Optimum Solution Information\n",
        "print(\"Optimum Solution ID : \" + str(Best_Solution_id))\n",
        "print(\"Optimum Fitness : \" + str(max(finalFitness)))\n",
        "print(\"Optimum Solution File Size : \" + str(Best_Solution_Size) + \"MB\")\n",
        "print(\"Optimum Solution Duration : \" + str(Best_Solution_Duration) + \" Minutes\")\n",
        "print(\"Optimum Solution : \")\n",
        "print(str(Best_Solution[1:]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimum Solution ID : 1\n",
            "Optimum Fitness : 604\n",
            "Optimum Solution File Size : 4400MB\n",
            "Optimum Solution Duration : 604 Minutes\n",
            "Optimum Solution : \n",
            "file 1     1\n",
            "file 2     1\n",
            "file 3     1\n",
            "file 4     1\n",
            "file 5     1\n",
            "file 6     1\n",
            "file 7     0\n",
            "file 8     0\n",
            "file 9     0\n",
            "file 10    0\n",
            "Name: 1, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oT0xPUUOL3hq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def runs(run):\n",
        "  epoch = 0\n",
        "  #iteration and \n",
        "  iterations = 100\n",
        "\n",
        "  #n point for crossover\n",
        "  n_point = 3\n",
        "  #crossover rate\n",
        "  p_c = 0.8\n",
        "  #mutation rate\n",
        "  p_m = 0.01\n",
        "  \n",
        "  for i in range(1,run+1):\n",
        "    start = time.time()\n",
        "    trainModel(iterations,n_point,p_c,p_m)\n",
        "    end = time.time()\n",
        "    \n",
        "    per_run_time.append(end-start)\n",
        "    \n",
        "    #Final population Duration and Size storage\n",
        "    finalFitness = []\n",
        "    finalDuration = []\n",
        "    finalSize = []\n",
        "    \n",
        "    global chromosome\n",
        "    \n",
        "    for i in range(popSize):\n",
        "      fitness,duration,size = getFitness_for_runs(chromosome.iloc[i,1:].values)\n",
        "      finalDuration.append(duration)\n",
        "      finalFitness.append(fitness)\n",
        "      finalSize.append(size)\n",
        "    \n",
        "    higestFitness_index = finalFitness.index(max(finalFitness))\n",
        "    best_out_of_runs_fitness.append(finalFitness[higestFitness_index])\n",
        "    best_out_of_runs_duration.append(finalDuration[higestFitness_index])\n",
        "    best_out_of_runs_size.append(finalSize[higestFitness_index])\n",
        "    \n",
        "    chromosome = chromosome_backup\n",
        "    print(\"Completed Run : \" + str(epoch))\n",
        "    epoch += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8ikjXT8MDrl",
        "colab_type": "code",
        "outputId": "f037ac56-157f-4080-9343-3d205a876f38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "#population size\n",
        "popSize = 50 \n",
        "\n",
        "#creation of population of Chromosomes\n",
        "chromosome = createInitPop(popSize)\n",
        "chromosome_backup = chromosome\n",
        "\n",
        "run = 50\n",
        "best_out_of_runs_fitness = []\n",
        "best_out_of_runs_duration = []\n",
        "best_out_of_runs_size = []\n",
        "per_run_time = []\n",
        "runs(run)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Completed Run : 0\n",
            "Completed Run : 1\n",
            "Completed Run : 2\n",
            "Completed Run : 3\n",
            "Completed Run : 4\n",
            "Completed Run : 5\n",
            "Completed Run : 6\n",
            "Completed Run : 7\n",
            "Completed Run : 8\n",
            "Completed Run : 9\n",
            "Completed Run : 10\n",
            "Completed Run : 11\n",
            "Completed Run : 12\n",
            "Completed Run : 13\n",
            "Completed Run : 14\n",
            "Completed Run : 15\n",
            "Completed Run : 16\n",
            "Completed Run : 17\n",
            "Completed Run : 18\n",
            "Completed Run : 19\n",
            "Completed Run : 20\n",
            "Completed Run : 21\n",
            "Completed Run : 22\n",
            "Completed Run : 23\n",
            "Completed Run : 24\n",
            "Completed Run : 25\n",
            "Completed Run : 26\n",
            "Completed Run : 27\n",
            "Completed Run : 28\n",
            "Completed Run : 29\n",
            "Completed Run : 30\n",
            "Completed Run : 31\n",
            "Completed Run : 32\n",
            "Completed Run : 33\n",
            "Completed Run : 34\n",
            "Completed Run : 35\n",
            "Completed Run : 36\n",
            "Completed Run : 37\n",
            "Completed Run : 38\n",
            "Completed Run : 39\n",
            "Completed Run : 40\n",
            "Completed Run : 41\n",
            "Completed Run : 42\n",
            "Completed Run : 43\n",
            "Completed Run : 44\n",
            "Completed Run : 45\n",
            "Completed Run : 46\n",
            "Completed Run : 47\n",
            "Completed Run : 48\n",
            "Completed Run : 49\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwMVTezYX5il",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyI4ru--iVnx",
        "colab_type": "code",
        "outputId": "9a14a9c6-90c1-49fa-fc0d-131b249e8f35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#Getting the Optimum solution Information to Variables\n",
        "Best_Solution_id = best_out_of_runs_fitness.index(max(best_out_of_runs_fitness))\n",
        "Best_Solution_Size = best_out_of_runs_size[Best_Solution_id] \n",
        "Best_Solution_Duration = best_out_of_runs_duration[Best_Solution_id]\n",
        "\n",
        "print(\"Optimum Solution ID : \" + str(Best_Solution_id))\n",
        "print(\"Optimum Fitness : \" + str(max(finalFitness)))\n",
        "print(\"Optimum Solution File Size : \" + str(Best_Solution_Size) + \"MB\")\n",
        "print(\"Optimum Solution Duration : \" + str(Best_Solution_Duration) + \" Minutes\")\n",
        "\n",
        "#Best_Solution = chromosome.iloc[Best_Solution_id]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimum Solution ID : 41\n",
            "Optimum Fitness : 604\n",
            "Optimum Solution File Size : 4475MB\n",
            "Optimum Solution Duration : 611 Minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCHyDhYnm6BI",
        "colab_type": "code",
        "outputId": "6e9a0869-ba78-46dc-b197-d615fdbffcee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sum = 0\n",
        "for i in range(len(per_run_time)):\n",
        "  sum += per_run_time[i]\n",
        "\n",
        "avg = sum/len(per_run_time)\n",
        "\n",
        "print(\"Average Time : \" + str(avg))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Time : 4.473152079582214\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYDiYFofY9aW",
        "colab_type": "code",
        "outputId": "724def57-beb8-4e59-eb3e-9f7bd80130fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,run+1)],[per_run_time[x] for x in range(len(per_run_time))])\n",
        "plt.title(\"Runs vs Time elapsed\")\n",
        "plt.xlabel(\"Runs (100 iterations per Run)\")\n",
        "plt.ylabel(\"Time elapsed per Run\")\n",
        "plt.savefig('time-50-runs-standard-elitism.pdf')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e03wTXSiwR6tuYX2ohsZCVcvAq3q0loHDeBZLnjwL95GqhqRRnu1y6MUq4Vtv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\nYsbCMAzDiIsZC8MwDCMuZiwMwzCMuPw/qaGmMTqwoIAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUAL28uzPx6b",
        "colab_type": "code",
        "outputId": "f390a8f8-672d-4ed0-89f1-2a77ce201613",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,run+1)],[best_out_of_runs_fitness[x] for x in range(len(best_out_of_runs_fitness))])\n",
        "plt.title(\"Runs vs Fitness\")\n",
        "plt.xlabel(\"Runs (100 iterations per Run)\")\n",
        "plt.ylabel(\"Best Fitness per Run\")\n",
        "plt.savefig('best-fitess-runs-standard-elitism.pdf')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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siGiLiLbx48c3cqqZDSObJvkdYJrV211kB0laR7aT5dbpPel4bIGyVwGrImJR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\nWSkcYMzMrBQOMGZmVor/D7hvylbwsGzfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVvRI_CWQwlc",
        "colab_type": "code",
        "outputId": "85681bac-4ba5-4599-eede-8b2d724e24d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,run+1)],[best_out_of_runs_duration[x] for x in range(len(best_out_of_runs_duration))])\n",
        "plt.title(\"Runs vs Duration\")\n",
        "plt.xlabel(\"Runs (100 iterations per Run)\")\n",
        "plt.ylabel(\"Duration of the Best Fitness per Run in Minutes\")\n",
        "plt.savefig('best-duration-runs-standard-elitism.pdf')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D+XuqlXm1pEclLcyvqTldki7K598r6cAidTQza6TUmbrIxnoWWVOKdJFV7gfTBTwKnFqw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\nwJiZWVs4wJiZWVs4wJiZWVv8/+GMFcgapboTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFZdyWhJRBBJ",
        "colab_type": "code",
        "outputId": "bd405821-186c-4868-a39b-36982d9cbe91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,run+1)],[best_out_of_runs_size[x] for x in range(len(best_out_of_runs_size))])\n",
        "plt.title(\"Runs vs File Size\")\n",
        "plt.xlabel(\"Runs (100 iterations per Run)\")\n",
        "plt.ylabel(\"File Size of the Best Fitness per Run in MB\")\n",
        "plt.savefig('best-size-runs-standard-elitism.pdf')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TGlFExICsy11VfalzV9XzBxFIRMSgpXO8P0mzETFntTvHN0+Noye5WhExZ7WGR9lsnpi3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\nTokAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeuUuq1QQ837",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "highest_fitness_index = best_out_of_runs_fitness.index(max(best_out_of_runs_fitness))\n",
        "opt_duration = best_out_of_runs_duration[highest_fitness_index]\n",
        "opt_size = best_out_of_runs_size[highest_fitness_index]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVNrWts2Xsq-",
        "colab_type": "code",
        "outputId": "fb546de6-99ec-412a-f6bd-fe767cbb7cff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(\"Optimum Duration : \" + str(opt_duration))\n",
        "print(\"Optimum Size : \" + str(opt_size))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimum Duration : 611\n",
            "Optimum Size : 4475\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGw3JHBKp1_J",
        "colab_type": "text"
      },
      "source": [
        "### **Plotting the Final Result**\n",
        "\n",
        "This plot shows 3 import facts about the final solution. \n",
        "\n",
        "\n",
        "1.   The Horizontal Line shows the 4,500MB benchmark line\n",
        "2.   The Zig Zag plot shows the Fitness of the Final Population\n",
        "3. The Red Cross (+) shows the position of the Optimum solution Generated\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DYhr1UnHHC0",
        "colab_type": "code",
        "outputId": "bfa089a6-d300-444d-b1ba-527e3ea3c4cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "plt.plot([x for x in range(popSize)],[finalSize[x] for x in range(len(finalSize))])\n",
        "plt.legend(\"Evolved Generation\")\n",
        "plt.plot([Best_Solution_id],[Best_Solution_Size],'r+')\n",
        "plt.legend(\"Optimum Solution\")\n",
        "plt.plot([x for x in range(popSize)],[4500 for x in range(len(finalSize))])\n",
        "plt.legend(\"Benchmark\")\n",
        "plt.xlabel(\"Chromosome in the Evolved Population\")\n",
        "plt.ylabel(\"File Size in MB\")\n",
        "plt.title(\"Chromosome in the Evolved Population vs File Size\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Chromosome in the Evolved Population vs File Size')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m5ekflDStnXmu5DYOM7M1DcQ7x98dERMjYnIePxGYGRE7AzPzOMBBwM75cyxw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\nzMya8v8A9zpo5XPj+FgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BuTJqts4rHoz",
        "colab_type": "text"
      },
      "source": [
        "### **Variation with  Iteration Count**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUi2K139rXTi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEb13q_VBegj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_avg_best(n,pop_1):\n",
        "  for j in range(1,n+1):\n",
        "    finalFitness = []\n",
        "    finalDuration = []\n",
        "    finalSize = []\n",
        "    \n",
        "    #standard values\n",
        "    n_point = 1\n",
        "    p_c = 0.8\n",
        "    p_m = 0.01\n",
        "    \n",
        "    for i in range(1):\n",
        "      trainModel(j,n_point,p_c,p_m)\n",
        "\n",
        "      \n",
        "      for i in range(popSize):\n",
        "        fitness, duration,size = getFitness_for_runs(pop_1.iloc[i,1:].values)\n",
        "        finalFitness.append(fitness)\n",
        "        finalDuration.append(duration)\n",
        "        finalSize.append(size)\n",
        "    \n",
        "    highest_fit_id = finalFitness.index(max(finalFitness))\n",
        "    \n",
        "    best_vals_size.append(finalSize[highest_fit_id])\n",
        "    best_vals_duration.append(finalDuration[highest_fit_id])\n",
        "    best_vals_fitness.append(finalFitness[highest_fit_id])\n",
        "    \n",
        "    print(\"Completed : \" + str(j))\n",
        "    pop_1 = chromosome"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOimnDW0RMlN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "chromosome = createInitPop(popSize)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJQMeoa5EFAs",
        "colab_type": "code",
        "outputId": "9ad149ef-ce9e-49d1-dc72-5a531d99e4a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1717
        }
      },
      "source": [
        "\n",
        "pop_1 = chromosome\n",
        "#save the best chromosome fitness per iteration\n",
        "best_vals_size = [ ]\n",
        "best_vals_duration = [ ]\n",
        "best_vals_fitness = [ ]\n",
        "n=100\n",
        "get_avg_best(n,pop_1)\n"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Completed : 1\n",
            "Completed : 2\n",
            "Completed : 3\n",
            "Completed : 4\n",
            "Completed : 5\n",
            "Completed : 6\n",
            "Completed : 7\n",
            "Completed : 8\n",
            "Completed : 9\n",
            "Completed : 10\n",
            "Completed : 11\n",
            "Completed : 12\n",
            "Completed : 13\n",
            "Completed : 14\n",
            "Completed : 15\n",
            "Completed : 16\n",
            "Completed : 17\n",
            "Completed : 18\n",
            "Completed : 19\n",
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            "Completed : 22\n",
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            "Completed : 24\n",
            "Completed : 25\n",
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            "Completed : 35\n",
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            "Completed : 37\n",
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            "Completed : 40\n",
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            "Completed : 42\n",
            "Completed : 43\n",
            "Completed : 44\n",
            "Completed : 45\n",
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            "Completed : 49\n",
            "Completed : 50\n",
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            "Completed : 54\n",
            "Completed : 55\n",
            "Completed : 56\n",
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            "Completed : 64\n",
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            "Completed : 72\n",
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            "Completed : 75\n",
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            "Completed : 77\n",
            "Completed : 78\n",
            "Completed : 79\n",
            "Completed : 80\n",
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            "Completed : 84\n",
            "Completed : 85\n",
            "Completed : 86\n",
            "Completed : 87\n",
            "Completed : 88\n",
            "Completed : 89\n",
            "Completed : 90\n",
            "Completed : 91\n",
            "Completed : 92\n",
            "Completed : 93\n",
            "Completed : 94\n",
            "Completed : 95\n",
            "Completed : 96\n",
            "Completed : 97\n",
            "Completed : 98\n",
            "Completed : 99\n",
            "Completed : 100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10C3by4qRLKz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfkU77cMCdMr",
        "colab_type": "code",
        "outputId": "50a60edf-6a68-4b22-8ec4-5c46a5d9f64c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,n+1)],best_vals_size)\n",
        "plt.plot([x for x in range(1,n+1)],[4500 for x in range(1,n+1)])\n",
        "plt.title(\"Iterations vs File Size of Fittest of each Generation\")\n",
        "plt.xlabel(\"Iterations/Generations\")\n",
        "plt.ylabel(\"File Size of Fitest of each Generation in MB\")\n",
        "plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('iterations-size.pdf')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ziMYdf9zM5knaGvhPdsNqmorLKsjPEy3zPONwzuWuOJ0cvgS8JKldmF4EjM92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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLdS3UIZDVDq",
        "colab_type": "code",
        "outputId": "f15a7a9a-4b71-4fa0-d635-70c2fc3aa3b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "plt.plot([x for x in range(1,n+1)],best_vals_duration)\n",
        "plt.title(\"Iterations vs Duration of Fittest of each Generation\")\n",
        "plt.xlabel(\"Iterations/Generations\")\n",
        "plt.ylabel(\"Duration Size of Fitest of each Generation in Minutes\")\n",
        "plt.savefig('iterations-duration.pdf')\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0VqolCFcxmVl59dlILWl/4ABgfUnfq5m1OlBpdGCtrNLtKiYzK796V7jnSPc7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\n9coZhJmZ9er/AxDOA/W+2mfvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01qEa1V4bedp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "f2aa8c02-cf66-4979-e5e8-7491dc145eb2"
      },
      "source": [
        "plt.plot([x for x in range(1,n+1)],best_vals_fitness)\n",
        "plt.title(\"Iterations vs Fitness of Fittest of each Generation\")\n",
        "plt.xlabel(\"Iterations/Generations\")\n",
        "plt.ylabel(\"Fitness of Fitest of each Generation in Minutes\")\n",
        "plt.savefig('iterations-fitness.pdf')\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SdoqIR4D9gAcbbHI1cImkc0mV1NOBOxu9x1DwTxfeya1zFrU6DGszR7x2m75XMltLjSqp\nZwH3A7WrX32tcgBvKrD/E4GLcwumucBxkt4BfAOYBPxS0t0RcWBEPCDpMlIiUgFOGA4tmB5+eimv\nmrwx+/395q0OxdrE6JEjONIJhA2CRgnEx4HDgReBS4GrImJZf3YeEXez5ngSAFflR0/rnwWc1Z/3\naLVKtcqMKRM4Zf8dWx2Kmdk61WsldUR8LSL2JuUCtgFuknSZpN0GLbphoLNSZdTIIvcbmpkNL0UG\nDJpL6o/petJ9Cf6rXKezGowa6fEezax8GlVSb0e6s/lQ0v0JlwJfjIgXBym2YaHSVWWMcxBmVkKN\n6iDmAPeScg8vAFOAj9TugI6Ic5se3RDXVQ2qAaNGOIEws/JplECcSWqtBLDhIMQy7HR2VQFcxGRm\npdToRrnPD2Icw1KlmtJPFzGZWRn5yrYWOivOQZhZeTmBWAud1VoC4dNoZuXjK9taqHTVipicgzCz\n8ulzPAhJY4F3AdPq14+IM5sX1vCwqpLarZjMrISKDBj0c2AJqZvvlc0NZ3jpzDkI10GYWRkVSSC2\njoi3ND2SYaiS6yDcisnMyqjIle33kl7V9EiGoc5KLQfhBMLMyqdIDmJv4FhJj5KKmARERPxDUyMb\nBla3YnIRk5mVT5EE4qCmRzFMrW7F5ByEmZVPo876NoqIF4ClgxjPsLK6FZNzEGZWPo1yEJcAB5Na\nLwUvH1FuuybGNSys7ovJOQgzK59GfTEdnJ+3HbxwhhcXMZlZmfnKthbcm6uZlZkTiLXQmXtzHe0E\nwsxKyAnEWqjkHMRoFzGZWQkVaeaKpJHAFqzZF9PjzQpquHAltZmVWZHO+k4EPgc8A1Tz7AB8o1yu\npB7tZq5mVkJFchAnAztFxOJmBzPcdLqIycxKrMiV7QlSb67WTcW9uZpZiRXJQcwFbpb0S+q6+46I\nc5sW1TBR64vJOQgzK6MiCcTj+TEmPyyr9ebqBMLMyqjPBCIivgAgacP8elmzgxouKtUqEox0JbWZ\nlVCff30lvVLSXcADwAOSZkvapfmhDX2dXcFoDzdqZiVV5Op2PvDxiJgaEVOBU4HvFdm5pAmSLpf0\nsKSHJO0paVNJN0j6S37eJK8rSV+XNEfSvZJmDPywBkdnV9V3UZtZaRVJIMZFxG9qLyLiZmBcwf2f\nB1wbEa8AdgUeAj4F3BQR04Gb8mtI405Mz4/jgW8XfI+WqXRVfZOcmZVWkavbXEmfkTQtP84gtWxq\nSNLGwD7AhQAR0RERzwOHAj/Iq/0AOCxPHwr8MJI7gAmStuzn8Qyqzmq4gtrMSqvI1e39wCTgyvyY\nlOf1ZVtgIfB9SXdJukDSOGCLiHgqr/M0qQsPgMmkey5q5ud5Q1ZnxUVMZlZeRVoxPQecNMB9zwBO\njIg/SDqP1cVJtX2HpOjPTiUdTyqCYsqUKQMIa92pVMM3yZlZaTUacvRrEXGKpF+Q+l5aQ0Qc0se+\n5wPzI+IP+fXlpATiGUlbRsRTuQjp2bx8AbBN3fZb53nd3/d8UsU5M2fO7Ffisq6lSmoXMZlZOTXK\nQfwoP58zkB1HxNOSnpC0U0Q8AuwHPJgfxwBn5+ef502uBj4q6VJgd2BJXVHUkNTZVXUzVzMrrUZD\njs7Ok7tFxHn1yySdDNxSYP8nAhdLGkOq2D6OVO9xmaQPAI8B78nr/gp4KzAHWJHXHdIqXS5iMrPy\nKtLVxjGk5qr1ju1h3stExN3AzB4W7dfDugGcUCCeIcOtmMyszBrVQRwFvBfYVtLVdYvGA39rdmDD\ngVsxmVmZNcpB/B54CpgIfLVu/lLg3mYGNVxUqlVGuQ7CzEqqUR3EY6Q6gj0HL5zhpbMrWH+MEwgz\nK6cinfXtIemPkpZJ6pDUJemFwQhuqEutmFzEZGblVOTv7zeBo4C/AOsDHwT+u5lBDRduxWRmZVao\nfCQi5gAjI6IrIr4PvKW5YQ0PnVXfKGdm5VWkmeuKfB/D3ZK+Qqq49lUR30ltZuVW5Or2T3m9jwLL\nSd1hvKuZQQ0Xla5glOsgzKykGuYgJI0EvhgRRwMvAV8YlKiGic6uYPQo5yDMrJwaXt0ioguYmouY\nrBu3YjKzMitSBzEXuC3fTb28NjMizm1aVMOER5QzszIrkkD8NT9GkLrZsMx9MZlZmRUZMOgLAJI2\niIgVzQ9p+EitmFzEZGblVORO6j0lPQg8nF/vKulbTY9siOuqBhG4LyYzK60iV7evAQcCiwEi4h5g\nn2YGNRx0dlUBGD3KOQgzK6eid1I/0W1WVxNiGVZWJRDOQZhZSRWppH5C0l5ASBoNnAw81Nywhr5K\nVxoO230xmVlZFfn7+2HSSG+TgQXAbgyzkd+aobOacxBuxWRmJVWkFdMi4OhBiGVY6cw5CLdiMrOy\n6jOBkDQJ+BAwrX79iHh/88Ia+iq5DsKtmMysrIrUQfwc+B1wI66cXmV1KyYnEGZWTkUSiA0i4rSm\nRzLMrCpicl9MZlZSRf7+XiPprU2PZJhZ3YrJOQgzK6ciV7eTSYnES5JekLTUY1JDR62IyZXUZlZS\nRVoxuYO+HlS63MzVzMqtSF9MkvQ+SZ/Jr7eR9Lrmhza0Vaq5iMl1EGZWUkX+/n4L2BN4b369DPjv\npkU0THS4FZOZlVyRVky7R8QMSXcBRMRzHmFudSW1+2Iys7IqcnXrzGNTB6y6ca7a1KiGgYp7czWz\nkiuSQHwduArYXNJZwK3AF5sa1TDQ4TupzazkirRiuljSbGA/QMBhEVGoN1dJ84ClpDuwKxExU9Ku\nwHeADYF5wNER8UJe/3TgA3n9kyLiun4f0SCpuC8mMyu5InUQRMTDkpYCIwEkjYqISsH3eGPu8K/m\nAuATEXGLpPcDnwQ+I2ln4EhgF2Ar4EZJO0bEkOzeo+LeXM2s5Hq9ukk6XdJn62b9HrgGuJ50UR+o\nHYHf5ukbgHfl6UOBSyNiZUQ8CswBhmxz2g6PB2FmJdfo7++7ga/Wvf5bRPwD6R/+2wruP4DrJc2W\ndHye9wApMai9xzZ5ejJQP3Ld/DxvDZKOlzRL0qyFCxcWDGPdq3hEOTMruYZXt4hYXvfyvDyvC1i/\n4P73jogZwEHACZL2Ad4P/Guu1xgPdPQn4Ig4PyJmRsTMSZMm9WfTdWpVHYTvgzCzkmp0ddswDzEK\nQERcBCBpLLBRkZ1HxIL8/CypJdTrIuLhiDggIl4D/Bj4a159AatzEwBb53lD0upWTC5iMrNyapRA\nXA58V9IGtRmSxpFaIF3e144ljZM0vm67A4D7JW2e540Azsj7A7gaOFLSWEnbAtOBO/t/SINjdSsm\n5yDMrJwaXd0+AzwLPJ7rEGaTmqU+k5f1ZQvgVkn3kC70v4yIa4GjJP0ZeBh4Evg+QEQ8AFwGPAhc\nC5wwVFswQWrFNEIw0jkIMyupXpu55ovzpyR9Adghz54TES8W2XFEzAV27WH+eeT6jB6WnQWcVWT/\nrdbRVfVYEGZWakVulHsRuG8QYhlWKl3h0eTMrNT8F3iAKl1Vt2Ays1LzFW6AOrrC/TCZWakV6mpD\n0mRgav36EfHb3rcov0pX1f0wmVmp9ZlASPoycASpdVGtVVGwuruMtlSphpu4mlmpFclBHAbsFBEr\nmx3McJJaMTkHYWblVeQv8FxgdJ9rtZlKV9X9MJlZqfWag5D0DVJR0grgbkk3AatyERFxUvPDG7oq\nXeHR5Mys1BoVMc3Kz7NJ3WBYnY6uqlsxmVmpNbqT+gewqh+ll2rdXuTxqccOTnhDV6Ur3IrJzEqt\nyF/gm1ize+/1gRubE87wUalW3YrJzEqtyBVuvYhYVnuRpzdosH5b6OgK98VkZqVW5Aq3XNKM2gtJ\nrwEKddhXZqkVk4uYzKy8itwHcQrwU0lPAgL+jnTjXFtLdRDOQZhZeRXpzfWPkl4B7JRnPRIRnc0N\na+jr9I1yZlZyhfpiIiUOOwPrATMkERE/bF5YQ1+nK6nNrOSK9MX0OWBfUgLxK+Ag4FagvROIipu5\nmlm5FfkLfDiwH/B0RBxHGiVu46ZGNQxUqh5RzszKrcgV7sWIqAIVSRuRxqneprlhDX2dHlHOzEqu\nSB3ELEkTgO+Rut1YBtze1KiGgc4u10GYWbkVacX0r3nyO5KuBTaKiHubG9bQV/GNcmZWcn1e4ZS8\nT9JnI2Ie8Lyk1zU/tKErInIrJhcxmVl5FfkL/C1gT+Co/Hop8N9Ni2gY6KoGEbiIycxKrUgdxO4R\nMUPSXQAR8ZykMU2Oa0irVAPAN8qZWakV+Qvcmbv4DgBJk4BqU6Ma4jq70uGPcQ7CzEqsyBXu68BV\nwOaSziLdJPfFpkY1xHV25RyEm7maWYkVacV0saTZpJvlBBwWEQ81PbIhrJJzEG7FZGZlVqgvpoh4\nGHi4ybEMG525DsJFTGZWZr7CDUBnpZaDcBGTmZVXrwmEpLYfd7o3laqLmMys/Bpd4W4HkPSjge5c\n0jxJ90m6W9KsPG83SXfU5tVuuss35H1d0hxJ99aPYjfU1CqpxzgHYWYl1qgOYoyk9wJ7SXpn94UR\ncWXB93hjRCyqe/0V4AsR8X+S3ppf70vqRnx6fuwOfDs/Dzm1Zq6jRjgHYWbl1SiB+DBwNDABeHu3\nZQEUTSC6C2CjPL0x8GSePhT4YUQEcIekCZK2jIinBvg+TbOqmatzEGZWYr0mEBFxK3CrpFkRceEA\n9x/A9ZIC+G5EnE8a4/o6SeeQirj2yutOBp6o23Z+nrdGAiHpeOB4gClTpgwwrLVT8Y1yZtYGijRz\n/ZGkk4B98utbgO8UHJd674hYIGlz4AZJD5MGIPpYRFwh6T3AhcD+RQPOicz5ADNnzoyi261Lq3MQ\nTiDMrLyKdtb3mvz8LWAGqX6gTxGxID8/S7ob+3XAMawunvppngewgDUHIto6zxtyOqtu5mpm5Vck\ngXhtRBwTEb/Oj+OA1/a1kaRxksbXpoEDgPtJdQ5vyKu9CfhLnr4a+OfcmmkPYMlQrH+ANBYEuIjJ\nzMqtSBFTl6TtI+KvAJK2A7oKbLcFcJWk2vtcEhHXSloGnCdpFPASuT4B+BXwVmAOsAI4rl9HMohW\ntWJyDsLMSqxIAvFJ4DeS5pL6YppKgYt3RMwFdu1h/q2kIqvu8wM4oUA8LedmrmbWDop01neTpOnA\nTnnWIxGxsrlhDW0uYjKzdlC0s76VQNuPQ13jIiYzawf+CzwAnR5RzszagBOIAfCNcmbWDvq8wkm6\nqci8dtLpAYPMrA30WgchaT1gA2CipE1ILZgg9aM0eRBiG7I85KiZtYNGldT/Quo3aStgNqsTiBeA\nbzY5riGt1opptHMQZlZijTrrO490Q9uJEfGNQYxpyOvsqjJCMNI5CDMrsSJ/gZ+u6zLjDElXDuXB\nfAZDZ7Xq+gczK70iV7nPRMRSSXuTel29kIKd9ZVVpSvcgsnMSq/IVa7W79LbgPMj4pfAmOaFNPR1\ndlV9D4SZlV6RBGKBpO8CRwC/kjS24Hal1dkV7ofJzEqvyFXuPcB1wIER8TywKakDv7bV2VVljHMQ\nZlZyfSYQEbECeBbYO8+qsHoMh7ZU6XIltZmVX5E7qT8HnAacnmeNBv63mUENdZ3VcB2EmZVekb/B\n7wAOAZYDRMSTwPhmBjXUdVaqbsVkZqVX5CrXkQfzCVg1fGhbqzgHYWZtoEgCcVluxTRB0oeAG4EL\nmhvW0NbZVXUrJjMrvSIjyp0j6c2kPph2Aj4bETc0PbIhLLVicgJhZuXWZwIh6csRcRpwQw/z2lKl\nKxgzygmEmZVbkavcm3uYd9C6DmQ46ayGe3I1s9JrNB7ER4B/BbaTVD8e9XjgtmYHNpR1VqqMdiW1\nmZVcoyKmS4D/A74EfKpu/tKI+FtToxriKlVXUptZ+TVKICIi5kk6ofsCSZu2cyJR6QpGuw7CzEqu\nrxzEwaTR5ILVI8qRX2/XxLiGtI6uKqM9WJCZlVyjEeUOzs/bDl44w0OlyzfKmVn59VpOIumjddO7\nDE44w0OlWnUrJjMrvUZXuffXTf+o2YEMJx0VJxBmVn593iiXtV15SqWrymlX3MfCZStftmx5Rxej\nXAdhZiXXKIGYIOkdpFzGRpLeWb8wIq5samQtNm/xCq7403y2nTiOjdcfvcayXbfemDfsNKlFkZmZ\nDY5GCcQtpG6+AX4LvL1uWQB9JhCS5gFLSeNaVyJipqSfkPp0ApgAPB8Ru+X1Twc+kNc/KSKuK34o\n69binHP490Nfyd7TJ7YqDDPQGmkOAAAPRElEQVSzlmnUium4dfQeb4yIRXX7PaI2LemrwJI8vTNw\nJLALsBVwo6QdI6JrHcXRL4uWdQAwcfyYVry9mVnLtaymVZJI413/OM86FLg0IlZGxKPAHOB1rYpv\n8fKUg5i44dhWhWBm1lLNTiACuF7SbEnHd1v2j8AzEVEb33oy8ETd8vl53hokHS9plqRZCxcubErQ\nAIuWrmSEYJMNnIMws/bU6D6Id+fntblRbu+ImEHq/fUESfvULTuK1bmHwiLi/IiYGREzJ01qXkXx\nwmUdbDpuDCPdWsnM2lSjHMTp+fmKge48Ihbk52eBq8hFRpJGAe8EflK3+gJgm7rXW+d5LbF42Uo2\nG+fiJTNrX41aMS2WdD2wraSruy+MiEN62GaVPHb1iIhYmqcPAM7Mi/cHHo6I+XWbXA1cIulcUiX1\ndODO4oeybi1attIV1GbW1holEG8DZpDuov7qAPa9BXBVqotmFHBJRFyblx1Jt+KliHhA0mXAg0AF\nOKFVLZgAFi/vYNdNJrTq7c3MWq5RM9cO4A5Je0XEQkkb5vnLiuw4IuYCu/ay7Nhe5p8FnFVk/822\naOlKt2Ays7ZWpBXTFpLuAh4AHswtkl7Z5Lha6sWOLpZ3dLmIyczaWpEE4nzg4xExNSKmAKfmeaW1\nKN9FPdGV1GbWxookEOMi4je1FxFxMzCuaRENAasSCOcgzKyNFenNda6kz7C6y+/3AXObF1LrLc7d\nbLiZq5m1syI5iPcDk0id810BTGTNsSJKZ3UOwgmEmbWvPnMQEfEccNIgxDJkLF5ey0G4iMnM2peH\nRevBwqUrGT92FOuNHtnqUMzMWsYJRA/SXdQuXjKz9uYEogeLl3W4eMnM2l6fCYSkr0jaSNJoSTdJ\nWijpfYMRXKssWua7qM3MiuQgDoiIF4CDgXnADsAnmxlUqy1e3sFmGzoHYWbtrUgCUWvp9DbgpxGx\npInxtFylq8pzKzqcgzCztlfkRrlrJD0MvAh8RNIk4KXmhtU6f1vRQQRMdA7CzNpcnzmIiPgUsBcw\nMyI6geWk8aNLadHSdA+EcxBm1u6KVFK/G+iMiC5JZwD/SxrQp5R8F7WZWVKkDuIzeVS4vUkjwV0I\nfLu5YbXO4uUpgXAzVzNrd0USiNqobm8Dzo+IXwKlvXquKmJyDsLM2lyRBGKBpO8CRwC/kjS24HbD\n0qLlKxkzcgTjxxapvzczK68iF/r3ANcBB0bE88CmlPg+iEVLO5i44RjyWNpmZm2rSCumFcCzwN55\nVgX4SzODaqXFy1eymVswmZkVasX0OeA04PQ8azSpJVMppW42SlvFYmZWWJEipncAh5DufyAingTG\nNzOoVkpFTM5BmJkVSSA6IiKAAJBU2vGoI8JFTGZmWZEE4rLcimmCpA8BNwLfa25YrfHCixU6u8JF\nTGZmFBty9BxJbwZeAHYCPhsRNzQ9sia65c8L+Y9rHnzZ/Eo1AHezYWYGxTrrIycIwzpRqLfh2FFM\n32LDHpe9epsJvH6HiYMckZnZ0NNnAiHpncCXgc0B5UdExEZNjq1pXjN1E14z9TWtDsPMbEgrkoP4\nCvD2iHio2cGYmdnQUaSS+hknDmZm7adIDmKWpJ8APwNW1mZGxJVNi8rMzFquSA5iI2AFcADw9vw4\nuMjOJc2TdJ+kuyXNqpt/oqSHJT0g6St180+XNEfSI5IO7N+hmJnZulQkB3FBRNxWP0PS6/vxHm+M\niEV1276RNCLdrhGxUtLmef7OwJHALqQBiW6UtGNEdPW0UzMza64iOYhvFJxX1EeAsyNiJUBEPJvn\nHwpcGhErI+JRYA7wurV4HzMzWwu95iAk7Ukai3qSpI/XLdoIGFlw/wFcLymA70bE+cCOwD9KOgt4\nCfhERPwRmAzcUbft/Dyve1zHA8cDTJkypWAYZmbWX42KmMYAG+Z16jvnewE4vOD+946IBbkY6QZJ\nD+f9bQrsAbyW1JXHdkUDzonM+QAzZ86MotuZmVn/9JpARMQtwC2SLoqIxway84hYkJ+flXQVqcho\nPnBl7gDwTklVYCKwANimbvOt87xezZ49e5Gk/sQ2EVjU51rl047H3Y7HDO153O14zLB2xz21yEpK\n1+keFkhfi4hTJP2C3JNrvYg4pOGOU6+vIyJiaZ6+ATgTmAZsFRGflbQjcBMwBdgZuISUiGyV509f\nl5XUkmZFxMx1tb/hoh2Pux2PGdrzuNvxmGFwjrtREdOP8vM5A9z3FsBVeejOUcAlEXGtpDHA/0i6\nH+gAjsm5iQckXQY8SBq17gS3YDIza51GCcRCWFXU1G8RMRfYtYf5HcD7etnmLOCsgbyfmZmtW42a\nuf6sNiHpikGIZTCc3+oAWqQdj7sdjxna87jb8ZhhEI67UR3EXRHx6u7TZmbWHhrlIKKXaTMzawON\nchBdwHLS+A/rk/pjghKMB2FmZn3rNQcRESMjYqOIGB8Ro/J07fWwSxwkvSV3AjhH0qdaHU8zSNpG\n0m8kPZg7Qjw5z99U0g2S/pKfN2l1rM0gaaSkuyRdk19vK+kP+TP/SW5BVxqSJki6PHd8+ZCkPdvh\ns5b0sfz9vl/SjyWtV7bPWtL/SHo2t/aszevxs1Xy9Xzs90qasa7iKNIX07AnaSTw38BBpPstjsqd\nA5ZNBTg1InYm3al+Qj7OTwE3RcR00v0lpUwggZOB+rFLvgz8V0TsADwHfKAlUTXPecC1EfEKUovB\nhyj5Zy1pMnASMDMiXknq9udIyvdZXwS8pdu83j7bg4Dp+XE88O11FURbJBCkm+/mRMTc3Mz2UlLn\ngKUSEU9FxJ/y9FLSBWMy6Vh/kFf7AXBYayJsHklbA28DLsivBbwJuDyvUqrjlrQxsA9wIaTm4xHx\nPG3wWZOa568vaRSwAfAUJfusI+K3wN+6ze7tsz0U+GEkdwATJG25LuJolwRiMvBE3eseOwIsE0nT\ngFcDfwC2iIin8qKnSTcxls3XgH8Dqvn1ZsDzEVHJr8v2mW9Lulfp+7lY7YLcY0GpP+vcfc85wOOk\nhGEJMJtyf9Y1vX22Tbu+tUsC0VYkbQhcAZwSES/UL8t3rZeqVZqkg4FnI2J2q2MZRKOAGcC3cxP0\n5XQrTirpZ70J6R/ztqQuecbx8qKY0husz7ZdEoh+dwQ4XEkaTUocLq4bFvaZWpYzPz/b2/bD1OuB\nQyTNIxUfvolUPj8hF0NA+T7z+cD8iPhDfn05KcEo+2e9P/BoRCyMiE7gStLnX+bPuqa3z7Zp17d2\nSSD+CEzPLR3GkCq1rm5xTOtcLne/EHgoIs6tW3Q1cEyePgb4+WDH1kwRcXpEbB0R00if7a8j4mjg\nN6zumr5Uxx0RTwNPSNopz9qP1I9ZqT9rUtHSHpI2yN/32nGX9rOu09tnezXwz7k10x7AkrqiqLXS\n630QZSPpraRy6pHA/+R+n0pF0t7A74D7WF0W/2lSPcRlpF5zHwPeExHdK8BKQdK+pEGoDlYaZ+RS\n0vgjdwHvq41kWAaSdiNVyo8B5gLHkf70lfqzlvQF4AhSq727gA+SytxL81lL+jGwL6lL72eAz5G6\nP3rZZ5sTym+SitpWAMdFxKx1Eke7JBBmZtY/7VLEZGZm/eQEwszMeuQEwszMeuQEwszMeuQEwszM\neuQEwlpK0rL8PE3Se9fxvj/d7fXv1+X+8z63lHR9np4u6RpJf5U0O/esu8+6fs9+xHZYfaeUks6U\ntH+r4rHhxwmEDRXTgH4lEHV3zvZmjQQiIvbqZ0xFvAW4TtJ6wC+B8yNi+4h4DXAisF0T3nOV3FNx\nbw4j9V4MQER8NiJubGY8Vi5OIGyoOBv4R0l35/7+R0r6T0l/zH3c/wukG+Ek/U7S1aQ7aJH0s/yP\n/QFJx+d5Z5N6/Lxb0sV5Xi23orzv+yXdJ+mIun3frNVjLFycb0JC0tlK42zcK+mcurjfAvwfcDRw\ne0SsukM/Iu6PiIvy9uNyH/935s71Ds3zj5V0paRrcz//X6ltL+kASbdL+pOkn+Y+tpA0T9KXJf0J\neLekD+XzdI+kK/JdxnsBhwD/mc/B9pIuknR43sd+OY77clxj6/b9hfye90l6RZ7/hryfu/N249fZ\nJ29DV0T44UfLHsCy/LwvcE3d/OOBM/L0WGAWqYO2fUkd021bt+6m+Xl94H5gs/p99/Be7wJuIN1V\nvwWp+4Yt876XkPqyGQHcDuxN6hn2EVbfWDohP48E7s7T5wInNzjOL5Lu7gWYAPyZ1NHcsaS7oDcG\n1iPdIbsN6Q7a3wLj8janAZ/N0/OAf6vb92Z10/8BnJinLwIOr1t2Eak7ivVIvX/umOf/kNSxY23f\nte3/FbggT/8CeH2e3hAY1ervjh/NfzgHYUPVAaT+Ze4mdRWyGWlAFIA7I+LRunVPknQPcAfp4jqd\nxvYGfhwRXRHxDHAL8Nq6fc+PiCpwN6noawnwEnChpHeyevjd3XNsLyPpqpxDqXWYeADwqXw8N5Mu\n0lPyspsiYklEvETKFU0lDfi0M3Bb3uaYPL/mJ3XTr8y5qvtIOZld+jj+nUgd3v05v/4BaWyJmlrM\ns/PxA9wGnCvpJFICWcFKr68yXLNWEemf7HVrzEx9LS3v9np/YM+IWCHpZtLFd6Dq++/pIv1Trkh6\nHaljuMOBj5J6jD0IuDav+wB1F9mIeIekmaSxC2rH866IeKTb8eze03vm9W+IiKN6iXN53fRFwGER\ncY+kY0k5obVRi6cWCxFxtqRfAm8lJVoHRsTDa/k+NsQ5B2FDxVKgvlz7OuAjSt2XI2lHpQFxutsY\neC4nDq8g/fOu6axt383vgCNyPcck0oX9zt4Cy2X/G0fEr4CPkYb3hJRg1Cp9LwFeL+mQuk036HY8\nJ9bVaby6t/fL7sj72yGvP07Sjr2sOx54Kh/r0XXzu5/TmkeAabV9A/9EykX1StL2EXFfRHyZ1Dvy\nK/qI30rACYQNFfcCXbmi9WOkXkofBP6kNHD7d+k5x3stMErSQ6SK7jvqlp0P3FurpK5zVX6/e4Bf\nk8rzn24Q23jgGkn3ArcCH88Jy0uRhnYlIl4EDgY+LGmupNuBM0h1AgD/DozO8TyQX/cqIhaS6id+\nnN/3dnq/KH+GVNR1G1D/r/5S4JO5Unn7un2/ROr59ae5WKoKfKdRPMApucjsXqCTVDFvJefeXM0G\nQNL7gK0j4uxWx2LWLE4gzMysRy5iMjOzHjmBMDOzHjmBMDOzHjmBMDOzHjmBMDOzHjmBMDOzHv1/\nppj0W3cj9nQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSGCCVIccx-1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import statistics as st"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Kktg8DidKf3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "cdb46c87-e766-42c5-84a0-2fabfacb06fc"
      },
      "source": [
        "print('Average Size of Fittest :' + str(st.mean(best_vals_size)) + 'MB')\n",
        "print('Average Duration of Fittest :' + str(st.mean(best_vals_duration)) + ' minutes')\n",
        "print('Average Fitness of Fittest :' + str(st.mean(best_vals_fitness)))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Size of Fittest :4402MB\n",
            "Average Duration of Fittest :608 minutes\n",
            "Average Fitness of Fittest :608\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2X8B3_zO6ik",
        "colab_type": "text"
      },
      "source": [
        "### **Variation with Crossover Rate** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-k7KkA7KKHyG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_avg_best_pc_var(start,end,step,pop_1):\n",
        "  epoch = 1\n",
        "  \n",
        "  n_point = 1\n",
        "  p_m = 0.01\n",
        "  \n",
        "  for j in frange(start,end,step):\n",
        "    finalFitness = []\n",
        "    finalDuration = []\n",
        "    finalSize = []\n",
        "    for k in range(10):\n",
        "      \n",
        "      trainModel(iteration_pc+1,n_point,j,p_m)\n",
        "    \n",
        "      \n",
        "      for i in range(popSize):\n",
        "        fitness, duration,size = getFitness_for_runs(pop_1.iloc[i,1:].values)\n",
        "        finalFitness.append(fitness)\n",
        "        finalDuration.append(duration)\n",
        "        finalSize.append(size)\n",
        "        \n",
        "    id = finalFitness.index(max(finalFitness))\n",
        "    best_vals_size.append(finalSize[id])\n",
        "    best_vals_duration.append(finalDuration[id])\n",
        "    best_vals_fitness.append(finalFitness[id])\n",
        "    \n",
        "    print(\"Completed : \" + str(epoch) + \" Best Size :\" + str(finalSize[finalDuration.index(max(finalDuration))]) + \" Best Duration : \" + str(max(finalDuration)))\n",
        "    pop_1 = chromosome\n",
        "    epoch +=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBCYCTRbQTze",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def frange(start, stop, step):\n",
        "  i = start\n",
        "  while i < stop:\n",
        "    yield i\n",
        "    i += step"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnH89FK_QyoD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "chromosome = createInitPop(popSize)\n",
        "pop_1 = chromosome\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qx5c3k-pQ4R5",
        "colab_type": "code",
        "outputId": "d50e5038-b8ec-462b-cb6e-cd8ec11fe931",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1717
        }
      },
      "source": [
        "best_vals_size = [ ]\n",
        "best_vals_duration = [ ]\n",
        "best_vals_fitness = [ ]\n",
        "iteration_pc = 10\n",
        "start =0\n",
        "end = 1\n",
        "step = 0.01\n",
        "get_avg_best_pc_var(start,end,step,pop_1)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Completed : 1 Best Size :8025 Best Duration : 1017\n",
            "Completed : 2 Best Size :5675 Best Duration : 782\n",
            "Completed : 3 Best Size :5725 Best Duration : 787\n",
            "Completed : 4 Best Size :5825 Best Duration : 791\n",
            "Completed : 5 Best Size :5725 Best Duration : 787\n",
            "Completed : 6 Best Size :5675 Best Duration : 709\n",
            "Completed : 7 Best Size :6475 Best Duration : 819\n",
            "Completed : 8 Best Size :5500 Best Duration : 744\n",
            "Completed : 9 Best Size :5950 Best Duration : 729\n",
            "Completed : 10 Best Size :5775 Best Duration : 781\n",
            "Completed : 11 Best Size :6575 Best Duration : 819\n",
            "Completed : 12 Best Size :5675 Best Duration : 777\n",
            "Completed : 13 Best Size :4825 Best Duration : 651\n",
            "Completed : 14 Best Size :6075 Best Duration : 757\n",
            "Completed : 15 Best Size :7275 Best Duration : 901\n",
            "Completed : 16 Best Size :6225 Best Duration : 766\n",
            "Completed : 17 Best Size :5975 Best Duration : 824\n",
            "Completed : 18 Best Size :6575 Best Duration : 902\n",
            "Completed : 19 Best Size :5525 Best Duration : 767\n",
            "Completed : 20 Best Size :5975 Best Duration : 824\n",
            "Completed : 21 Best Size :5975 Best Duration : 733\n",
            "Completed : 22 Best Size :5975 Best Duration : 733\n",
            "Completed : 23 Best Size :5675 Best Duration : 777\n",
            "Completed : 24 Best Size :5975 Best Duration : 824\n",
            "Completed : 25 Best Size :5975 Best Duration : 733\n",
            "Completed : 26 Best Size :6575 Best Duration : 902\n",
            "Completed : 27 Best Size :6575 Best Duration : 902\n",
            "Completed : 28 Best Size :4475 Best Duration : 613\n",
            "Completed : 29 Best Size :5675 Best Duration : 777\n",
            "Completed : 30 Best Size :4950 Best Duration : 663\n",
            "Completed : 31 Best Size :5275 Best Duration : 734\n",
            "Completed : 32 Best Size :5975 Best Duration : 733\n",
            "Completed : 33 Best Size :4475 Best Duration : 613\n",
            "Completed : 34 Best Size :8075 Best Duration : 1022\n",
            "Completed : 35 Best Size :7275 Best Duration : 997\n",
            "Completed : 36 Best Size :5675 Best Duration : 777\n",
            "Completed : 37 Best Size :5225 Best Duration : 713\n",
            "Completed : 38 Best Size :5675 Best Duration : 777\n",
            "Completed : 39 Best Size :5200 Best Duration : 629\n",
            "Completed : 40 Best Size :6825 Best Duration : 859\n",
            "Completed : 41 Best Size :5975 Best Duration : 824\n",
            "Completed : 42 Best Size :6575 Best Duration : 902\n",
            "Completed : 43 Best Size :5675 Best Duration : 777\n",
            "Completed : 44 Best Size :5675 Best Duration : 777\n",
            "Completed : 45 Best Size :5275 Best Duration : 734\n",
            "Completed : 46 Best Size :7475 Best Duration : 944\n",
            "Completed : 47 Best Size :5200 Best Duration : 629\n",
            "Completed : 48 Best Size :4475 Best Duration : 613\n",
            "Completed : 49 Best Size :5675 Best Duration : 777\n",
            "Completed : 50 Best Size :5975 Best Duration : 733\n",
            "Completed : 51 Best Size :6575 Best Duration : 902\n",
            "Completed : 52 Best Size :5675 Best Duration : 777\n",
            "Completed : 53 Best Size :5275 Best Duration : 734\n",
            "Completed : 54 Best Size :4475 Best Duration : 613\n",
            "Completed : 55 Best Size :5675 Best Duration : 777\n",
            "Completed : 56 Best Size :5675 Best Duration : 777\n",
            "Completed : 57 Best Size :5975 Best Duration : 733\n",
            "Completed : 58 Best Size :5975 Best Duration : 824\n",
            "Completed : 59 Best Size :5825 Best Duration : 802\n",
            "Completed : 60 Best Size :7175 Best Duration : 897\n",
            "Completed : 61 Best Size :5625 Best Duration : 772\n",
            "Completed : 62 Best Size :5225 Best Duration : 713\n",
            "Completed : 63 Best Size :5675 Best Duration : 777\n",
            "Completed : 64 Best Size :5275 Best Duration : 734\n",
            "Completed : 65 Best Size :5275 Best Duration : 734\n",
            "Completed : 66 Best Size :5575 Best Duration : 683\n",
            "Completed : 67 Best Size :4575 Best Duration : 639\n",
            "Completed : 68 Best Size :6575 Best Duration : 902\n",
            "Completed : 69 Best Size :5175 Best Duration : 632\n",
            "Completed : 70 Best Size :4725 Best Duration : 647\n",
            "Completed : 71 Best Size :5225 Best Duration : 713\n",
            "Completed : 72 Best Size :6225 Best Duration : 767\n",
            "Completed : 73 Best Size :4725 Best Duration : 647\n",
            "Completed : 74 Best Size :5975 Best Duration : 733\n",
            "Completed : 75 Best Size :4475 Best Duration : 613\n",
            "Completed : 76 Best Size :5250 Best Duration : 634\n",
            "Completed : 77 Best Size :5175 Best Duration : 703\n",
            "Completed : 78 Best Size :4475 Best Duration : 613\n",
            "Completed : 79 Best Size :4475 Best Duration : 613\n",
            "Completed : 80 Best Size :5225 Best Duration : 713\n",
            "Completed : 81 Best Size :5350 Best Duration : 639\n",
            "Completed : 82 Best Size :4475 Best Duration : 613\n",
            "Completed : 83 Best Size :5975 Best Duration : 733\n",
            "Completed : 84 Best Size :5975 Best Duration : 733\n",
            "Completed : 85 Best Size :5125 Best Duration : 698\n",
            "Completed : 86 Best Size :5200 Best Duration : 629\n",
            "Completed : 87 Best Size :5675 Best Duration : 777\n",
            "Completed : 88 Best Size :5275 Best Duration : 734\n",
            "Completed : 89 Best Size :5975 Best Duration : 733\n",
            "Completed : 90 Best Size :4575 Best Duration : 639\n",
            "Completed : 91 Best Size :5075 Best Duration : 691\n",
            "Completed : 92 Best Size :5125 Best Duration : 698\n",
            "Completed : 93 Best Size :4475 Best Duration : 613\n",
            "Completed : 94 Best Size :4475 Best Duration : 613\n",
            "Completed : 95 Best Size :5275 Best Duration : 734\n",
            "Completed : 96 Best Size :5200 Best Duration : 629\n",
            "Completed : 97 Best Size :4475 Best Duration : 613\n",
            "Completed : 98 Best Size :4475 Best Duration : 613\n",
            "Completed : 99 Best Size :5275 Best Duration : 734\n",
            "Completed : 100 Best Size :4475 Best Duration : 613\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9exd6-YbdRE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "best_vals_duration"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUpXP7xJRrI9",
        "colab_type": "code",
        "outputId": "25664af1-35c8-496b-b2ab-adc5e6709270",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in frange(start,end,step)],best_vals_size)\n",
        "plt.plot([x for x in frange(start,end,step)],[4500 for x in frange(start,end,step)])\n",
        "plt.title(\"Crossover Rate vs File Size of the Fittest\")\n",
        "plt.xlabel(\"Crossover Rate\")\n",
        "plt.ylabel(\"File Size of the Fitness\")\n",
        "plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('cr-size.pdf')"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HA/PM7KE85a9FSVviKFh33ATlXlXlnMuzTFedJ4EKAEl9gVeAzYEzJY3JfdZa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\nc1nxwOGccy4rHjicc85l5f8DhDujxb2op/wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RezM3IkDSP7q",
        "colab_type": "code",
        "outputId": "51abe6e5-3e7f-4943-acfa-e75cc2e37550",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in frange(start,end,step)],best_vals_duration)\n",
        "plt.title(\"Crossover Rate vs Duration of the Fittest\")\n",
        "plt.xlabel(\"Crossover Rate\")\n",
        "plt.ylabel(\"Duration of the Fitness\")\n",
        "#plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('cr-duration.pdf')"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A9yXNl3SppLE1yu8CLKl4vDQvG/Ja1rcgfCa1mTVOPRPEcGAacFFEHAysAc7d3JVKOkvS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\nwszMqnKCMDOzqpwgzMysqv8PyGezsTBHNzMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NbQEQHoiSSFO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6b398304-008c-46a5-d642-bd907bdd751b"
      },
      "source": [
        "plt.plot([x for x in frange(start,end,step)],best_vals_fitness)\n",
        "plt.title(\"Crossover Rate vs Fitness of the Fittest\")\n",
        "plt.xlabel(\"Crossover Rate\")\n",
        "plt.ylabel(\"Fitness of the Fitness\")\n",
        "#plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('cr-fitness.pdf')"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BTBzrBGFmzam/LqavR8RHgW9VnfVto4h4Y6GRDbG2jS0InyHOzJpTf3+PL03X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\ns5qcIMzMrKb/D0XjFqmQHkd6AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0n55j96yxYvA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5caa7f90-c770-4f51-d179-17babe351dec"
      },
      "source": [
        "print('Average Size of Fittest :' + str(st.mean(best_vals_size)) + 'MB')\n",
        "print('Average Duration of Fittest :' + str(st.mean(best_vals_duration)) + ' minutes')\n",
        "print('Average Fitness of Fittest :' + str(st.mean(best_vals_fitness)))"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Size of Fittest :4458MB\n",
            "Average Duration of Fittest :611 minutes\n",
            "Average Fitness of Fittest :611\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giRsY-LtTOKG",
        "colab_type": "text"
      },
      "source": [
        "### **Variation with Mutation Rate**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2OfL-ZpTQbV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_avg_best_pm_var(start,end,step,pop_1):\n",
        "  epoch = 1\n",
        "  n_point = 1\n",
        "  p_c = 0.8\n",
        "  for j in frange(start,end,step):\n",
        "    trainModel(iteration_pm+1,n_point,p_c,j)\n",
        "    \n",
        "    finalDuration = []\n",
        "    finalSize = []\n",
        "    finalFitness = []\n",
        "    for i in range(popSize):\n",
        "      fitness, duration,size = getFitness_for_runs(pop_1.iloc[i,1:].values)\n",
        "      finalDuration.append(duration)\n",
        "      finalSize.append(size)\n",
        "      finalFitness.append(fitness)\n",
        "      \n",
        "    best_fitness_id = finalFitness.index(max(finalFitness))  \n",
        "    best_vals_size.append(finalSize[best_fitness_id])\n",
        "    best_vals_duration.append(finalDuration[best_fitness_id])\n",
        "    best_vals_fitness.append(finalFitness[best_fitness_id])\n",
        "    print(\"Completed : \" + str(epoch) + \" Best Size :\" + str(finalSize[finalDuration.index(max(finalDuration))]) + \" Best Duration : \" + str(max(finalDuration)))\n",
        "    pop_1 = chromosome\n",
        "    epoch +=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3gGFHFyTXZQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "chromosome = createInitPop(popSize)\n",
        "pop_1 = chromosome"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-x6wG7iUTZ0V",
        "colab_type": "code",
        "outputId": "0da46429-4f7f-4363-c309-ee463af88d19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1717
        }
      },
      "source": [
        "best_vals_size = [ ]\n",
        "best_vals_duration = [ ]\n",
        "best_vals_fitness = [ ]\n",
        "iteration_pm = 10\n",
        "start =0\n",
        "end = 1\n",
        "step = 0.01\n",
        "get_avg_best_pm_var(start,end,step,pop_1)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Completed : 1 Best Size :7725 Best Duration : 982\n",
            "Completed : 2 Best Size :7725 Best Duration : 982\n",
            "Completed : 3 Best Size :7725 Best Duration : 982\n",
            "Completed : 4 Best Size :4350 Best Duration : 596\n",
            "Completed : 5 Best Size :5725 Best Duration : 691\n",
            "Completed : 6 Best Size :5550 Best Duration : 658\n",
            "Completed : 7 Best Size :4350 Best Duration : 596\n",
            "Completed : 8 Best Size :4325 Best Duration : 599\n",
            "Completed : 9 Best Size :6450 Best Duration : 794\n",
            "Completed : 10 Best Size :4675 Best Duration : 642\n",
            "Completed : 11 Best Size :5275 Best Duration : 649\n",
            "Completed : 12 Best Size :5125 Best Duration : 629\n",
            "Completed : 13 Best Size :5400 Best Duration : 648\n",
            "Completed : 14 Best Size :4400 Best Duration : 606\n",
            "Completed : 15 Best Size :6350 Best Duration : 781\n",
            "Completed : 16 Best Size :4400 Best Duration : 606\n",
            "Completed : 17 Best Size :4425 Best Duration : 614\n",
            "Completed : 18 Best Size :4425 Best Duration : 614\n",
            "Completed : 19 Best Size :5175 Best Duration : 634\n",
            "Completed : 20 Best Size :4425 Best Duration : 614\n",
            "Completed : 21 Best Size :4425 Best Duration : 614\n",
            "Completed : 22 Best Size :4875 Best Duration : 656\n",
            "Completed : 23 Best Size :6450 Best Duration : 783\n",
            "Completed : 24 Best Size :5375 Best Duration : 653\n",
            "Completed : 25 Best Size :5825 Best Duration : 724\n",
            "Completed : 26 Best Size :4425 Best Duration : 614\n",
            "Completed : 27 Best Size :5275 Best Duration : 636\n",
            "Completed : 28 Best Size :4425 Best Duration : 614\n",
            "Completed : 29 Best Size :4425 Best Duration : 614\n",
            "Completed : 30 Best Size :6350 Best Duration : 768\n",
            "Completed : 31 Best Size :4425 Best Duration : 614\n",
            "Completed : 32 Best Size :4525 Best Duration : 632\n",
            "Completed : 33 Best Size :4425 Best Duration : 614\n",
            "Completed : 34 Best Size :7075 Best Duration : 897\n",
            "Completed : 35 Best Size :4425 Best Duration : 614\n",
            "Completed : 36 Best Size :5150 Best Duration : 624\n",
            "Completed : 37 Best Size :5425 Best Duration : 674\n",
            "Completed : 38 Best Size :4425 Best Duration : 614\n",
            "Completed : 39 Best Size :4425 Best Duration : 614\n",
            "Completed : 40 Best Size :4425 Best Duration : 614\n",
            "Completed : 41 Best Size :4425 Best Duration : 614\n",
            "Completed : 42 Best Size :4425 Best Duration : 614\n",
            "Completed : 43 Best Size :6075 Best Duration : 757\n",
            "Completed : 44 Best Size :4425 Best Duration : 614\n",
            "Completed : 45 Best Size :4425 Best Duration : 614\n",
            "Completed : 46 Best Size :4425 Best Duration : 614\n",
            "Completed : 47 Best Size :4425 Best Duration : 614\n",
            "Completed : 48 Best Size :4425 Best Duration : 614\n",
            "Completed : 49 Best Size :5775 Best Duration : 714\n",
            "Completed : 50 Best Size :4425 Best Duration : 614\n",
            "Completed : 51 Best Size :5625 Best Duration : 688\n",
            "Completed : 52 Best Size :4425 Best Duration : 614\n",
            "Completed : 53 Best Size :4425 Best Duration : 614\n",
            "Completed : 54 Best Size :5575 Best Duration : 684\n",
            "Completed : 55 Best Size :8175 Best Duration : 1039\n",
            "Completed : 56 Best Size :4425 Best Duration : 614\n",
            "Completed : 57 Best Size :4425 Best Duration : 614\n",
            "Completed : 58 Best Size :4425 Best Duration : 614\n",
            "Completed : 59 Best Size :7075 Best Duration : 887\n",
            "Completed : 60 Best Size :4425 Best Duration : 614\n",
            "Completed : 61 Best Size :4425 Best Duration : 614\n",
            "Completed : 62 Best Size :4425 Best Duration : 614\n",
            "Completed : 63 Best Size :5025 Best Duration : 624\n",
            "Completed : 64 Best Size :4425 Best Duration : 614\n",
            "Completed : 65 Best Size :5775 Best Duration : 724\n",
            "Completed : 66 Best Size :4425 Best Duration : 614\n",
            "Completed : 67 Best Size :4425 Best Duration : 614\n",
            "Completed : 68 Best Size :5350 Best Duration : 651\n",
            "Completed : 69 Best Size :4425 Best Duration : 614\n",
            "Completed : 70 Best Size :6350 Best Duration : 781\n",
            "Completed : 71 Best Size :4425 Best Duration : 614\n",
            "Completed : 72 Best Size :5075 Best Duration : 699\n",
            "Completed : 73 Best Size :5650 Best Duration : 684\n",
            "Completed : 74 Best Size :5975 Best Duration : 824\n",
            "Completed : 75 Best Size :5325 Best Duration : 739\n",
            "Completed : 76 Best Size :4825 Best Duration : 664\n",
            "Completed : 77 Best Size :6350 Best Duration : 768\n",
            "Completed : 78 Best Size :5175 Best Duration : 717\n",
            "Completed : 79 Best Size :4425 Best Duration : 614\n",
            "Completed : 80 Best Size :5925 Best Duration : 734\n",
            "Completed : 81 Best Size :4425 Best Duration : 614\n",
            "Completed : 82 Best Size :4425 Best Duration : 614\n",
            "Completed : 83 Best Size :6575 Best Duration : 819\n",
            "Completed : 84 Best Size :5775 Best Duration : 794\n",
            "Completed : 85 Best Size :5025 Best Duration : 692\n",
            "Completed : 86 Best Size :5025 Best Duration : 692\n",
            "Completed : 87 Best Size :5725 Best Duration : 787\n",
            "Completed : 88 Best Size :5125 Best Duration : 709\n",
            "Completed : 89 Best Size :4425 Best Duration : 614\n",
            "Completed : 90 Best Size :4425 Best Duration : 614\n",
            "Completed : 91 Best Size :4425 Best Duration : 614\n",
            "Completed : 92 Best Size :4425 Best Duration : 614\n",
            "Completed : 93 Best Size :4425 Best Duration : 614\n",
            "Completed : 94 Best Size :5025 Best Duration : 692\n",
            "Completed : 95 Best Size :5325 Best Duration : 739\n",
            "Completed : 96 Best Size :4425 Best Duration : 614\n",
            "Completed : 97 Best Size :4425 Best Duration : 614\n",
            "Completed : 98 Best Size :5325 Best Duration : 739\n",
            "Completed : 99 Best Size :4425 Best Duration : 614\n",
            "Completed : 100 Best Size :4425 Best Duration : 614\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZlA2HChTb_r",
        "colab_type": "code",
        "outputId": "e7e5cfdb-1079-4a39-85b0-d3e1fdb9101e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in frange(start,end,step)],best_vals_size)\n",
        "plt.plot([x for x in frange(start,end,step)],[4500 for x in frange(start,end,step)])\n",
        "plt.title(\"Mutation Rate vs Size of Fittest\")\n",
        "plt.xlabel(\"Mutation Rate\")\n",
        "plt.ylabel(\"File size of Fittest Solution\")\n",
        "plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('mr-size.pdf')"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IoXMuhUUdHfcMgmldXw7L0hMXUvKquB03I90Th3MudUU5A55HMJHTLWa2LJz3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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOVvhifxTf2F",
        "colab_type": "code",
        "outputId": "b04cd7ad-7cd6-4477-b911-8004f6c41bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in frange(start,end,step)],best_vals_duration)\n",
        "plt.title(\"Mutation Rate vs Duration of Fittest\")\n",
        "plt.xlabel(\"Mutation Rate\")\n",
        "plt.ylabel(\"Duration of Fittest Solution\")\n",
        "plt.savefig('mr-duration.pdf')"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lc5Z5VkQcXbZ+uqSlRQVk1W0YqM9NTGZWrDw1iDWSXlZakfRSYE1xIVk1G2aTcw3CzIqV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\nKnKCMDOzipwgzMysov8Hf1cm4DjhPBwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iENuia_oVHI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "f9280560-7d19-4463-83bb-fd57cc7dbcef"
      },
      "source": [
        "plt.plot([x for x in frange(start,end,step)],best_vals_fitness)\n",
        "plt.title(\"Mutation Rate vs Fitness of Fittest\")\n",
        "plt.xlabel(\"Mutation Rate\")\n",
        "plt.ylabel(\"Fitness of Fittest Solution\")\n",
        "plt.savefig('mr-fitness.pdf')"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aOt2CMLPmVu0MeFX699u1CGS0aV3d7VXizKypVUsQS2FdV1PTaevs9kR9ZtbU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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEEuAI86ovDP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "184d0f06-26cf-41aa-976f-7804cc61cff5"
      },
      "source": [
        "print('Average Size of Fittest :' + str(st.mean(best_vals_size)) + 'MB')\n",
        "print('Average Duration of Fittest :' + str(st.mean(best_vals_duration)) + ' minutes')\n",
        "print('Average Fitness of Fittest :' + str(st.mean(best_vals_fitness)))"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Size of Fittest :4409MB\n",
            "Average Duration of Fittest :611 minutes\n",
            "Average Fitness of Fittest :611\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHTlkBOZou_U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrNs6j6zou8B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nUxFNg0GHCh6",
        "colab_type": "text"
      },
      "source": [
        "### **Variation with Population size**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ps0lewE_T3uT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pop_var(popSize):\n",
        "  epoch = 1\n",
        "  for j in range(1,popSize+1):\n",
        "    chromosome = createInitPop(j)\n",
        "    #n point for crossover\n",
        "    n_point = 1\n",
        "    #crossover rate\n",
        "    p_c = 0.8\n",
        "    #mutation rate\n",
        "    p_m = 0.01\n",
        "\n",
        "    finalDuration = []\n",
        "    finalSize = []\n",
        "    finalFitness = []\n",
        "    trainModel(100,n_point,p_c,p_m)\n",
        "    \n",
        "    #Get Fitness and Size information for the Last Population\n",
        "    for i in range(j):\n",
        "      fitness, duration,size = getFitness_for_runs(pop_1.iloc[i,1:].values)\n",
        "      finalDuration.append(duration)\n",
        "      finalSize.append(size)\n",
        "      finalFitness.append(fitness)\n",
        "      \n",
        "    best_fitness_id = finalFitness.index(max(finalFitness))  \n",
        "    best_vals_size.append(finalSize[best_fitness_id])\n",
        "    best_vals_duration.append(finalDuration[best_fitness_id])\n",
        "    best_vals_fitness.append(finalFitness[best_fitness_id])\n",
        "      \n",
        "    print(\"Completed : \" + str(epoch) + \" Pop Size : \" + str(j) + \" Best Size :\" + str(finalSize[finalDuration.index(max(finalDuration))]) + \" Best Duration : \" + str(max(finalDuration)))\n",
        "    epoch += 1\n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQ0krwKDJDnN",
        "colab_type": "code",
        "outputId": "3aecc9b3-ad04-4b55-f39d-601a74c63668",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        }
      },
      "source": [
        "#Final population Duration and Size storage\n",
        "best_vals_size = [ ]\n",
        "best_vals_duration = [ ]\n",
        "best_vals_fitness = [ ]\n",
        "\n",
        "popSize = 50\n",
        "pop_var(popSize)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Completed : 1 Pop Size : 1 Best Size :4350 Best Duration : 503\n",
            "Completed : 2 Pop Size : 2 Best Size :4425 Best Duration : 614\n",
            "Completed : 3 Pop Size : 3 Best Size :4425 Best Duration : 614\n",
            "Completed : 4 Pop Size : 4 Best Size :4425 Best Duration : 614\n",
            "Completed : 5 Pop Size : 5 Best Size :4425 Best Duration : 614\n",
            "Completed : 6 Pop Size : 6 Best Size :4425 Best Duration : 614\n",
            "Completed : 7 Pop Size : 7 Best Size :4425 Best Duration : 614\n",
            "Completed : 8 Pop Size : 8 Best Size :4425 Best Duration : 614\n",
            "Completed : 9 Pop Size : 9 Best Size :4425 Best Duration : 614\n",
            "Completed : 10 Pop Size : 10 Best Size :4425 Best Duration : 614\n",
            "Completed : 11 Pop Size : 11 Best Size :4425 Best Duration : 614\n",
            "Completed : 12 Pop Size : 12 Best Size :4425 Best Duration : 614\n",
            "Completed : 13 Pop Size : 13 Best Size :4425 Best Duration : 614\n",
            "Completed : 14 Pop Size : 14 Best Size :4425 Best Duration : 614\n",
            "Completed : 15 Pop Size : 15 Best Size :4425 Best Duration : 614\n",
            "Completed : 16 Pop Size : 16 Best Size :4425 Best Duration : 614\n",
            "Completed : 17 Pop Size : 17 Best Size :4425 Best Duration : 614\n",
            "Completed : 18 Pop Size : 18 Best Size :4425 Best Duration : 614\n",
            "Completed : 19 Pop Size : 19 Best Size :4425 Best Duration : 614\n",
            "Completed : 20 Pop Size : 20 Best Size :4425 Best Duration : 614\n",
            "Completed : 21 Pop Size : 21 Best Size :4425 Best Duration : 614\n",
            "Completed : 22 Pop Size : 22 Best Size :4425 Best Duration : 614\n",
            "Completed : 23 Pop Size : 23 Best Size :4425 Best Duration : 614\n",
            "Completed : 24 Pop Size : 24 Best Size :4425 Best Duration : 614\n",
            "Completed : 25 Pop Size : 25 Best Size :4425 Best Duration : 614\n",
            "Completed : 26 Pop Size : 26 Best Size :4425 Best Duration : 614\n",
            "Completed : 27 Pop Size : 27 Best Size :4425 Best Duration : 614\n",
            "Completed : 28 Pop Size : 28 Best Size :4425 Best Duration : 614\n",
            "Completed : 29 Pop Size : 29 Best Size :5300 Best Duration : 633\n",
            "Completed : 30 Pop Size : 30 Best Size :4425 Best Duration : 614\n",
            "Completed : 31 Pop Size : 31 Best Size :4425 Best Duration : 614\n",
            "Completed : 32 Pop Size : 32 Best Size :4425 Best Duration : 614\n",
            "Completed : 33 Pop Size : 33 Best Size :4425 Best Duration : 614\n",
            "Completed : 34 Pop Size : 34 Best Size :4425 Best Duration : 614\n",
            "Completed : 35 Pop Size : 35 Best Size :5225 Best Duration : 631\n",
            "Completed : 36 Pop Size : 36 Best Size :4425 Best Duration : 614\n",
            "Completed : 37 Pop Size : 37 Best Size :5075 Best Duration : 699\n",
            "Completed : 38 Pop Size : 38 Best Size :4425 Best Duration : 614\n",
            "Completed : 39 Pop Size : 39 Best Size :4425 Best Duration : 614\n",
            "Completed : 40 Pop Size : 40 Best Size :4425 Best Duration : 614\n",
            "Completed : 41 Pop Size : 41 Best Size :4425 Best Duration : 614\n",
            "Completed : 42 Pop Size : 42 Best Size :4425 Best Duration : 614\n",
            "Completed : 43 Pop Size : 43 Best Size :5400 Best Duration : 638\n",
            "Completed : 44 Pop Size : 44 Best Size :4425 Best Duration : 614\n",
            "Completed : 45 Pop Size : 45 Best Size :4425 Best Duration : 614\n",
            "Completed : 46 Pop Size : 46 Best Size :4425 Best Duration : 614\n",
            "Completed : 47 Pop Size : 47 Best Size :5725 Best Duration : 776\n",
            "Completed : 48 Pop Size : 48 Best Size :4425 Best Duration : 614\n",
            "Completed : 49 Pop Size : 49 Best Size :4425 Best Duration : 614\n",
            "Completed : 50 Pop Size : 50 Best Size :4425 Best Duration : 614\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIf0Bh-oJLkW",
        "colab_type": "code",
        "outputId": "08a7aeea-5f2a-452a-923c-0e745b0b72f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,popSize+1)],best_vals_size)\n",
        "plt.plot([x for x in range(1,popSize+1)],[4500 for i in range(1,popSize+1)])\n",
        "plt.title(\"Population Size vs Size of Fittest\")\n",
        "plt.xlabel(\"Population Size\")\n",
        "plt.ylabel(\"File size of Fittest Solution\")\n",
        "plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('popsize-size.pdf')"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kIaAdXyWn4Kvk0hn4MF0eAYyPiNXAXElzgMHAyxsbX108pLqZWc3qmgHwT5tg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\nWU7+fzHyRion42QFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIZMDXb-RSF0",
        "colab_type": "code",
        "outputId": "b2af9989-9f2d-4ef5-b83f-e4a49d887bee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "best_vals_size[len(best_vals_size)-1]"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4425"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okUmTuZVK9Dz",
        "colab_type": "code",
        "outputId": "05b5254e-af45-4479-e1eb-3a9972143cf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([x for x in range(1,popSize+1)],best_vals_duration)\n",
        "plt.title(\"Population Size vs Duration of Fittest\")\n",
        "plt.xlabel(\"Population Size\")\n",
        "plt.ylabel(\"Duration of Fittest Solution\")\n",
        "#plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('popsize-duration.pdf')"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dyh7ydBZwIPACcHRE3Lmy+x4uNzGZmS2v2Y1y7262YkRcWXAf+0TEktz8vwMnR8TPJB2Y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\nmVldDhBmZlaXA4SZmdXlAGFmZnX9H5j8P4X2a+fzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzyGDU6ttkHN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "96e138ac-d42f-42de-ab08-b027ab3b26b3"
      },
      "source": [
        "plt.plot([x for x in range(1,popSize+1)],best_vals_fitness)\n",
        "plt.title(\"Population Size vs Fitness of Fittest\")\n",
        "plt.xlabel(\"Population Size\")\n",
        "plt.ylabel(\"Fitness of Fittest Solution\")\n",
        "#plt.legend(['Results obtained','4,500MB Benchmark'])\n",
        "plt.savefig('popsize-fitness.pdf')"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wet3EZGbWTNeWkFkfhJuYzMwa6d4A0dvnm+TMzJro2hLSVzGZmTXXtSVk1knt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\nDhBmZlaXA4SZmdXlAGFmZnX9PzqYLsS7EBpZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v45wbk-_LI-i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "57621952-d580-419b-b390-b2870a45462b"
      },
      "source": [
        "print('Average Size of Fittest :' + str(st.mean(best_vals_size)) + 'MB')\n",
        "print('Average Duration of Fittest :' + str(st.mean(best_vals_duration)) + ' minutes')\n",
        "print('Average Fitness of Fittest :' + str(st.mean(best_vals_fitness)))"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Size of Fittest :4423MB\n",
            "Average Duration of Fittest :611 minutes\n",
            "Average Fitness of Fittest :611\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3towenYQhxf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpO0AXAPmYnI",
        "colab_type": "text"
      },
      "source": [
        "### **Brute Force**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgBE9ta8uNd5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate(variables,power):\n",
        "  val = int((2**variables)/(2**(power+1)))\n",
        "  file_10 = []\n",
        "  for i in range(val):\n",
        "    for j in range(2**power):\n",
        "      file_10.append(0)\n",
        "\n",
        "    for j in range(2**power):\n",
        "      file_10.append(1)\n",
        "      \n",
        "  return file_10\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8vJo_TQZ_aA6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "list_ret = []\n",
        "variables = 10\n",
        "for power in range(variables-1,-1,-1):\n",
        "  list_ret.append(generate(variables,power))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxNFimd_x_9x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "values ={\n",
        "    \n",
        "    'file ' + str(x+1):list_ret[x] for x in range(variables-1,-1,-1)\n",
        "    \n",
        "    \n",
        "    \n",
        "  }\n",
        "  #convert the array of population to a dataframe\n",
        "values = pd.DataFrame(values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3e39LhojyYoD",
        "colab_type": "code",
        "outputId": "5e6d70b4-4367-4b04-9162-10421df5e028",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        }
      },
      "source": [
        "values"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file 10</th>\n",
              "      <th>file 9</th>\n",
              "      <th>file 8</th>\n",
              "      <th>file 7</th>\n",
              "      <th>file 6</th>\n",
              "      <th>file 5</th>\n",
              "      <th>file 4</th>\n",
              "      <th>file 3</th>\n",
              "      <th>file 2</th>\n",
              "      <th>file 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1000</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1001</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1004</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1005</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1006</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1007</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1009</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1010</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1011</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1012</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1013</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1014</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1015</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1016</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1017</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1018</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1019</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1020</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1022</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1023</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      file 10  file 9  file 8  file 7  ...  file 4  file 3  file 2  file 1\n",
              "0           0       0       0       0  ...       0       0       0       0\n",
              "1           1       0       0       0  ...       0       0       0       0\n",
              "2           0       1       0       0  ...       0       0       0       0\n",
              "3           1       1       0       0  ...       0       0       0       0\n",
              "4           0       0       1       0  ...       0       0       0       0\n",
              "5           1       0       1       0  ...       0       0       0       0\n",
              "6           0       1       1       0  ...       0       0       0       0\n",
              "7           1       1       1       0  ...       0       0       0       0\n",
              "8           0       0       0       1  ...       0       0       0       0\n",
              "9           1       0       0       1  ...       0       0       0       0\n",
              "10          0       1       0       1  ...       0       0       0       0\n",
              "11          1       1       0       1  ...       0       0       0       0\n",
              "12          0       0       1       1  ...       0       0       0       0\n",
              "13          1       0       1       1  ...       0       0       0       0\n",
              "14          0       1       1       1  ...       0       0       0       0\n",
              "15          1       1       1       1  ...       0       0       0       0\n",
              "16          0       0       0       0  ...       0       0       0       0\n",
              "17          1       0       0       0  ...       0       0       0       0\n",
              "18          0       1       0       0  ...       0       0       0       0\n",
              "19          1       1       0       0  ...       0       0       0       0\n",
              "20          0       0       1       0  ...       0       0       0       0\n",
              "21          1       0       1       0  ...       0       0       0       0\n",
              "22          0       1       1       0  ...       0       0       0       0\n",
              "23          1       1       1       0  ...       0       0       0       0\n",
              "24          0       0       0       1  ...       0       0       0       0\n",
              "25          1       0       0       1  ...       0       0       0       0\n",
              "26          0       1       0       1  ...       0       0       0       0\n",
              "27          1       1       0       1  ...       0       0       0       0\n",
              "28          0       0       1       1  ...       0       0       0       0\n",
              "29          1       0       1       1  ...       0       0       0       0\n",
              "...       ...     ...     ...     ...  ...     ...     ...     ...     ...\n",
              "994         0       1       0       0  ...       1       1       1       1\n",
              "995         1       1       0       0  ...       1       1       1       1\n",
              "996         0       0       1       0  ...       1       1       1       1\n",
              "997         1       0       1       0  ...       1       1       1       1\n",
              "998         0       1       1       0  ...       1       1       1       1\n",
              "999         1       1       1       0  ...       1       1       1       1\n",
              "1000        0       0       0       1  ...       1       1       1       1\n",
              "1001        1       0       0       1  ...       1       1       1       1\n",
              "1002        0       1       0       1  ...       1       1       1       1\n",
              "1003        1       1       0       1  ...       1       1       1       1\n",
              "1004        0       0       1       1  ...       1       1       1       1\n",
              "1005        1       0       1       1  ...       1       1       1       1\n",
              "1006        0       1       1       1  ...       1       1       1       1\n",
              "1007        1       1       1       1  ...       1       1       1       1\n",
              "1008        0       0       0       0  ...       1       1       1       1\n",
              "1009        1       0       0       0  ...       1       1       1       1\n",
              "1010        0       1       0       0  ...       1       1       1       1\n",
              "1011        1       1       0       0  ...       1       1       1       1\n",
              "1012        0       0       1       0  ...       1       1       1       1\n",
              "1013        1       0       1       0  ...       1       1       1       1\n",
              "1014        0       1       1       0  ...       1       1       1       1\n",
              "1015        1       1       1       0  ...       1       1       1       1\n",
              "1016        0       0       0       1  ...       1       1       1       1\n",
              "1017        1       0       0       1  ...       1       1       1       1\n",
              "1018        0       1       0       1  ...       1       1       1       1\n",
              "1019        1       1       0       1  ...       1       1       1       1\n",
              "1020        0       0       1       1  ...       1       1       1       1\n",
              "1021        1       0       1       1  ...       1       1       1       1\n",
              "1022        0       1       1       1  ...       1       1       1       1\n",
              "1023        1       1       1       1  ...       1       1       1       1\n",
              "\n",
              "[1024 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCa8iJUp3cSd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def details(row):\n",
        "  duration = 0\n",
        "  size = 0\n",
        "  fitness = 0\n",
        "  for i in range(len(row)):\n",
        "    if(row[i] == 1):\n",
        "      size += df.loc[9-i,'size']\n",
        "      duration += df.loc[9-i,'duration']\n",
        "    \n",
        "    if(size<=4500):\n",
        "      fitness += duration\n",
        "    else:\n",
        "      fitness += duration*0.5\n",
        "  \n",
        "  return duration,size,fitness"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXlCOq800KIJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_duration = []\n",
        "final_fitness = []\n",
        "final_size = []\n",
        "for i in range(1024):\n",
        "  \n",
        "  duration,size,fitness = details(values.iloc[i].values)\n",
        "  final_duration.append(duration)\n",
        "  final_size.append(size)\n",
        "  final_fitness.append(fitness) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4I_jx7TybkG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nearst = 0\n",
        "index = -1\n",
        "for i in range(1024):\n",
        "  if(final_size[i] > nearst and final_size[i] <=4500):\n",
        "    nearst = final_size[i]\n",
        "    index = i"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBB0iG-G1OPP",
        "colab_type": "code",
        "outputId": "fe8918cb-18a2-4bcc-c84b-672c6208bd9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_size[index]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_T63a5I2xB6",
        "colab_type": "code",
        "outputId": "911a5cc7-3ccb-4169-9fb7-cff74c0631f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_duration[index]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "523"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGPprHor25Wm",
        "colab_type": "code",
        "outputId": "541022b3-4c13-42f3-943d-853a809b23e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "361"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9xYYN9h8mna",
        "colab_type": "code",
        "outputId": "baae3e7a-b991-4b8d-dfa5-ba5cd79bfcf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "final_fitness[index]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3090"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttaUL0WS6Kux",
        "colab_type": "code",
        "outputId": "8d032542-ea59-4256-d2dd-4f3d7ede45c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "values.iloc[index]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "file 10    1\n",
              "file 9     0\n",
              "file 8     0\n",
              "file 7     1\n",
              "file 6     0\n",
              "file 5     1\n",
              "file 4     1\n",
              "file 3     0\n",
              "file 2     1\n",
              "file 1     0\n",
              "Name: 361, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dzWWtwP6OSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XbQ4-DPC_Eds",
        "colab_type": "text"
      },
      "source": [
        "### **Differential Evolution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v5sDfKd_HWr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def mutate():\n",
        "  #Get 4 distinct values\n",
        "  state = True\n",
        "  a = 0\n",
        "  b = 0\n",
        "  c = 0\n",
        "  target_id = 0\n",
        "  scaleFactor = 0.5\n",
        "  while(state):\n",
        "    a = np.random.randint(0,popSize)\n",
        "    b = np.random.randint(0,popSize)\n",
        "    c = np.random.randint(0,popSize)\n",
        "    target_id = np.random.randint(0,popSize)\n",
        "    state = checkDistinct(a,b,c,target_id)\n",
        "    #print(\"a:\" +str(a) +\" b :\"+str(b) +\" c:\" + str(c) +\" target id:\" +str(target_id) )\n",
        "  #Get the respective solution items as arrays\n",
        "  a_item = population.iloc[a,1:].values\n",
        "  b_item = population.iloc[b,1:].values\n",
        "  c_item = population.iloc[c,1:].values\n",
        "  target_item = population.iloc[target_id,1:].values\n",
        "  #Add the weighted difference\n",
        "  for i in range(2):\n",
        "    a_item[i] = a_item[i] + scaleFactor*(b_item[i]-c_item[i])\n",
        "    \n",
        "  return a_item, target_item,target_id"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ij0vnppYBGa-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def crossover(donor,target):\n",
        "  #crossover rate\n",
        "  CR = 0.1\n",
        "  \n",
        "  #size of the arrays\n",
        "  size = len(donor)\n",
        "  \n",
        "  #trial vector\n",
        "  trial_vector = np.empty([size])\n",
        "  \n",
        "  #create an array with random values\n",
        "  random_array = [random.uniform(0,1) for x in range(size)]\n",
        "  for i in range(size):\n",
        "    if(random_array[i] <=CR or i == random.randint(1,size)):\n",
        "      #replace with donor\n",
        "      trial_vector[i] = donor[i]\n",
        "    else:\n",
        "      #replace with target\n",
        "      trial_vector[i] = target[i]\n",
        "  \n",
        "  return trial_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTIc92SoBKUd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def checkDistinct(a,b,c,d):\n",
        "  if(a != b != c !=d != a != c and b != d ):\n",
        "    return False\n",
        "  else:\n",
        "    return True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTkeZjMBBNam",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def selection(trial_vector,target):\n",
        "  fitness_trial,size_trial = getFitness(trial_vector)\n",
        "  fitness_target,size_target = getFitness(trial_vector)\n",
        "  \n",
        "  \n",
        "  if(fitness_trial>fitness_target):\n",
        "    target = trial_vector\n",
        "  else : \n",
        "    target = target\n",
        "  \n",
        "  return target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4et_MbNBQQm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def replace_to_population(offspring,target_id):\n",
        "  population.iloc[target_id,1:] = offspring"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4JeqFSUBUfy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def iterate(n):\n",
        "  #value = ackley_function(solution[0],solution[1])\n",
        "  for i in range(n):\n",
        "    donor, target,target_id = mutate()\n",
        "    trial_vector = crossover(donor,target)\n",
        "    offspring = selection(trial_vector,target)\n",
        "    replace_to_population(offspring,target_id)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuYxCX6CK8Yw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def details2(row):\n",
        "  duration = 0\n",
        "  size = 0\n",
        "  fitness = 0\n",
        "  for i in range(len(row)):\n",
        "    if(row[i] == 1):\n",
        "      size += df.loc[i,'size']\n",
        "      duration += df.loc[i,'duration']\n",
        "    \n",
        "    if(size<=4500):\n",
        "      fitness += duration\n",
        "    else:\n",
        "      fitness += duration*0.5\n",
        "  \n",
        "  return duration,size,fitness"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUxCaqBt_q9k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "popSize = 50\n",
        "population = createInitPop(popSize)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vMEcpBIBawr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iterations = 100\n",
        "iterate(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRcRCeWvKawF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvQTXM1HBcUc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "finalDuration = []\n",
        "finalFitness = []\n",
        "finalSize = []\n",
        "#Get Fitness and Size information for the Last Population\n",
        "for i in range(popSize):\n",
        "  duration,size,fitness = details2(population.iloc[i,1:].values)\n",
        "  finalDuration.append(duration)\n",
        "  finalFitness.append(fitness)\n",
        "  finalSize.append(size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBBu8t91DGnF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# nearst = 0\n",
        "# index = -1\n",
        "# for i in range(popSize):\n",
        "#   if(finalFitness[i] > nearst and finalSize[i] <=4500):\n",
        "#     nearst = final_size[i]\n",
        "#     index = i"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McmqkVFHJaOQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bf595eab-1683-40be-b80d-8f20e391047e"
      },
      "source": [
        "id = finalFitness.index(max(finalFitness))\n",
        "finalFitness[id]"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3678"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHDsez3zJjc0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a8a86489-58cf-4f2a-e526-067c8cc7c65f"
      },
      "source": [
        "finalSize[id]"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3950"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51BpFkk_KtV_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d7e26737-1f85-4c5e-8b1f-86b0f7d0a158"
      },
      "source": [
        "finalDuration[id]"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjpx9UGUFGry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "cae26cac-166e-4f13-90b6-037809b2952d"
      },
      "source": [
        "plt.plot([x for x in range(popSize)],finalFitness)"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd22e0590f0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cf/3zqbrOTysHAAAhA0XQ5VgGAwEXbDZSQkDtFvM2y0o8C7fDhmvGetDnc+JYjZVA/Hc1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\ngmKCAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RDsWF350ez1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "74af4528-ff21-4716-9ba2-c4302248666b"
      },
      "source": [
        "plt.plot([x for x in range(popSize)],finalDuration)"
      ],
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd22dfb23c8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7HUk3jWYL529m8MCx+Z7H+Il5d4Ba+kq9iXW1AmrSEAr0jLGXVT39bYyxDzDG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\nVZV2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGV16JYF0zpQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "50db97da-e705-47e9-93fe-777d01462cd9"
      },
      "source": [
        "plt.plot([x for x in range(popSize)],finalSize)\n",
        "plt.plot([x for x in range(popSize)],[4500 for x in range(popSize)])\n",
        "\n"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd22dfc95f8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HSbIePEUKK5kSXAQ4MBnqywCsZeUegCnVA5ATwTtZhXVuJYO75qPwe+VcW78ewDPKzr9X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\nsAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rD-uOGRj04hh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}