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Practise Genetic Algorithm in Python

single-objective minimization
built-in Individual and Operators for mathematical functions, travelling salesman problem
user-defined Individuals and Operators
based on Numpy

An example for solving the minimum solution of common test function schaffer N4 is shown below.

Import modules

from GA.GAPopulation.DecimalIndividual import DecimalFloatIndividual
from GA.GAPopulation.Population import Population
from GA.GAOperators.Selection import RouletteWheelSelection
from GA.GAOperators.Crossover import DecimalCrossover
from GA.GAOperators.Mutation import DecimalMutation
from GA.GAProcess import GA
import numpy as np

Initialize GA

# individual
dimension = 2
ranges = [(-10, 10)] * dimension
I = DecimalFloatIndividual(ranges)

# population
P = Population(I, 50)

# operators
S = RouletteWheelSelection()
C = DecimalCrossover([0.5, 0.9], 0.5) # adaptive crossover rate
# C = DecimalCrossover(0.9, 0.5) # constant crossover rate
M = DecimalMutation(0.12)

# GA
g = GA(P, S, C, M)

Solve

# schaffer-N4
# sol: x=[0,1.25313], min=0.292579
schaffer_n4 = lambda x: 0.5 + (np.cos(np.sin(abs(x[0]**2-x[1]**2)))**2-0.5) / (1.0+0.001*(x[0]**2+x[1]**2))**2

res = g.run(schaffer_n4, gen=800)   

x = [0,1.25313]
print('{0} : {1}'.format(res.evaluation, res.solution)) # 0.29257882535592317 : [1.25339239e+00 6.28576519e-05]
print('error: {:<3f} %'.format((res.evaluation/schaffer_n4(x)-1.0)*100)) # error: 0.000066 %

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