Multi dimension minimization (modification).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bitarray.util import int2ba, ba2int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ga.fabrics import BinaryFabric\n",
    "from ga.operators import BinaryOperator\n",
    "from ga.transformers import MultiDimentionTransformer\n",
    "\n",
    "from ga.evaluators import MinimumEvaluator\n",
    "from ga.cultivators import TournamentCultivator, SmartWieghtCultivator, EliteCultivator\n",
    "from ga.algorithms import SimpleGA\n",
    "\n",
    "from helpers.plotting import plotContour, plotScores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "f\\left(x_{1}, x_{2}\\right)=\\sin \\left(10 x_{1}\\right)+x_{1} \\cos \\left(2 \\pi x_{2}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "x_{1} \\in[-2,2], x_{2} \\in[0,1]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$f \\to min $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = lambda x: np.sin(10*x[0]) + x[0]*np.cos(2*np.pi*x[1])\n",
    "\n",
    "x1_int = [-2, 2]\n",
    "x2_int = [0, 1]\n",
    "\n",
    "x_ints = [x1_int, x2_int]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_x1 = 1e-7\n",
    "h_x2 = 1e-5\n",
    "\n",
    "n_x1 = len(int2ba(int((x1_int[1] - x1_int[0])/h_x1)))\n",
    "n_x2 = len(int2ba(int((x2_int[1] - x2_int[0])/h_x2)))\n",
    "\n",
    "n_xs = [n_x1, n_x2]\n",
    "\n",
    "n = sum(n_xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[26, 17]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100\n",
    "max_iterations = 100\n",
    "max_iterations_without_evolution = 20\n",
    "p_mutate = 0.6\n",
    "p_cross = 0.8\n",
    "p_elite = 0.05\n",
    "N_t = 5\n",
    "selection_pressure = 1e-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fabric = BinaryFabric(n)\n",
    "operator = BinaryOperator(n)\n",
    "transformer = MultiDimentionTransformer(n_xs, x_ints)\n",
    "evaluator = MinimumEvaluator(f, transformer)\n",
    "\n",
    "tCultivator = TournamentCultivator(N_t, p_mutate, p_cross, operator, evaluator)\n",
    "sCultivator = SmartWieghtCultivator(N_t, p_mutate, p_cross, operator, evaluator, selection_pressure)\n",
    "\n",
    "eCultivator = EliteCultivator(sCultivator, p_elite)\n",
    "\n",
    "ga = SimpleGA(fabric, evaluator, transformer, eCultivator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0%\n",
      "10%\n",
      "20%\n",
      "30%\n",
      "40%\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([1.7379514535956302, 0.5000038147263697], -2.732879966476537)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga.solve(N, max_iterations, max_iterations_without_evolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotContour(lambda x1, x2: f([x1, x2]), x1_int, x2_int, argmin=ga.solution)\n",
    "\n",
    "plotScores(ga.scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ga.best_from_populations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}