import numpy

​

# Parameter initialization 

genes = 2

chromosomes = 10

mattingPoolSize = 6

offspringSize = chromosomes - mattingPoolSize

lb = -5

ub = 5

populationSize = (chromosomes, genes)

generations = 3

​

#Population initialization

population = numpy.random.uniform(lb, ub, populationSize)

​

for generation in range(generations):

    print(("Generation:", generation+1))

    fitness = numpy.sum(population*population, axis=1)

    print("\npopulation")

    print(population)

    print("\nfitness calcuation")

    print(fitness)

    # Following statement will create an empty two dimensional array to store parents

    parents = numpy.empty((mattingPoolSize, population.shape[1]))

​

    # A loop to extract one parent in each iteration

    for p in range(mattingPoolSize):

        # Finding index of fittest chromosome in the population

        fittestIndex = numpy.where(fitness == numpy.max(fitness))