import numpy
genes = 2
chromosomes = 10
mattingtoolsize = 6
offspringsize = chromosomes - mattingpoolsize
lb = -5
ub = 5
populationsize = (chromosomes, genes)
generations = 3

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 calculation")
    print(fitness)
    parents = numpy.empty((mattingpoolsize, population.shape[1]))
    for p in range(mattingpoolsize):
        fittestindex = numpy.where(fitness == numpy.max(fitness))
        fittestindex = fittestindex[0][0]
        parents[p,:]=population[fittestindex, :]
        fitness[fittestindex] = -1
    print("\nParents :")
    print(parents)
    offspring = numpy.empty((offspringsize, population.shape[1]))
    for k in range(offspringsize):
        crossoverpoint = numpy.random.randint(0,genes)
        parent1index = k%parents.shape[0]
        parent2index = (k+1)%parents.shape[0]
        offspring[k,0:crossoverpoint] = parents[parent1index, 0: crossoverpoint]
        offspring[k, crossoverpoint:] = parents[parent2index, crossoverpoint:]