README.rst

geneticalgorithm2

geneticalgorithm2 is a Python library distributed on Pypi for implementing standard and elitist genetic-algorithm (GA). This package solves continuous, combinatorial and mixed optimization problems with continuous, discrete, and mixed variables. It provides an easy implementation of genetic-algorithm (GA) in Python.

Installation / PLEASE CHECK THE HOMEPAGE FOR CURRENT EXAMPLES OF USING

Use the package manager pip to install geneticalgorithm2 in Python.

pip install geneticalgorithm2

A simple example

Assume we want to find a set of X=(x1,x2,x3) that minimizes function f(X)=x1+x2+x3 where X can be any real number in [0,10]. This is a trivial problem and we already know that the answer is X=(0,0,0) where f(X)=0. We just use this simple example to see how to implement geneticalgorithm:

First we import geneticalgorithm and numpy. Next, we define function f which we want to minimize and the boundaries of the decision variables; Then simply geneticalgorithm is called to solve the defined optimization problem as follows:

import numpy as np
from geneticalgorithm2 import geneticalgorithm2 as ga

def f(X):
    return np.sum(X)


varbound=np.array([[0,10]]*3)

model=ga(function=f,dimension=3,variable_type='real',variable_boundaries=varbound)

model.run()

Notice that we define the function f so that its output is the objective function we want to minimize where the input is the set of X (decision variables). The boundaries for variables must be defined as a numpy array and for each variable we need a separate boundary. Here I have three variables and all of them have the same boundaries (For the case the boundaries are different see the example with mixed variables).