A sample Genetic Algorithm implementation in Python.

To use this project, you need to clone this module to your project folder.

1. Import the GeneticAlgorithm module by import GeneticAlgorithm as GA;

2. Set your own fitness function, the fitness function must accept the parameters that you want to optimize and return a fitness score;

3. Create a GeneticAlgorithm subject : ga = GA.GeneticAlgorithm(maxGen, popGeneration, pCross, pMutation, nGenes, parametersRange, fitness);

   maxGen # How many generation
   popGeneration # How many individuals in one geration
   pCross # The possibility of cross
   pMutation # The possibility of mutation
   nGenes  # How many genes (parameters to be optimized)
   parametersRange # The ranges of parameters, e.g. [[-1, 2, 0.01]，[-2, 3, 0.1]]
   fitness # The fitness function you defined

4. Run: ga.run()

5. Get result: ga.result()

import GeneticAlgorithm as GA # Import GeneticAlgorithm module
import math

# Set fitness function, par is the parameter list，ret is the fitness score
def fitness(par):
    x = par[0]
    ret = x*math.sin(10*math.pi*x)+2
    return ret

maxGen = 500 # How many generation
popGeneration = 50 # How many individuals in one geration
pCross = 0.1 # The possibility of cross
pMutation = 0.05 # The possibility of mutation
nGenes = 1 # How many genes (parameters to be optimized)
parametersRange = [[-1, 2, 0.01]] # The ranges of parameters, e.g. [[-1, 2, 0.01]，[-2, 3, 0.1]]

# Create a GA subject
ga = GA.GeneticAlgorithm(maxGen, popGeneration, pCross, pMutation, nGenes, parametersRange, fitness)
# Display subject information
ga.info()
# Run Genetic Algorithm
ga.run()
# Show result
ga.result()