import numpy
genes=2
chromosomes=10
mattingPoolSize=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:]
    print("\nOffspring after crossover:")
    print(offspring)
    
    for index in range(offspring.shape[0]):
        randomIndex=numpy.random.randint(1,genes)
        randomValue=numpy.random.uniform(lb,ub,1)
        offspring[index,randomIndex]=offspring[index,randomIndex]+randomValue
    print("\noffspring after Mutation")
    print(offspring)
    
    population[0:parents.shape[0],:]=parents
    population[parents.shape[0]:,:]=offspring
    print("\nNew population for next generation:")
    print(population)
    
fitness=numpy.sum(population*population,axis=1)
fittestIndex=numpy.where(fitness==numpy.max(fitness))
fittestIndex=fittestIndex[0][0]
fittestInd=population[fittestIndex,:]
bestFitness=fitness[fittestIndex]
print("\nBest Individual:")
print(fittestInd)
print("\nBest Individual's Fitness:")
print(bestFitness)