import numpy

def cal_pop_fitness(equation_inputs, pop):

# Calculating the fitness value of each solution in the current population.

# The fitness function calulates the sum of products between each input and its corresponding weight.

fitness = numpy.sum(pop*equation_inputs, axis=1)

return fitness

def select_mating_pool(pop, fitness, num_parents):

# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.

parents = numpy.empty((num_parents, pop.shape[1]))

for parent_num in range(num_parents):

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

max_fitness_idx = max_fitness_idx[0][0]

parents[parent_num, :] = pop[max_fitness_idx, :]

fitness[max_fitness_idx] = -99999999999

return parents

def crossover(parents, offspring_size):

offspring = numpy.empty(offspring_size)

# The point at which crossover takes place between two parents. Usually, it is at the center.

crossover_point = numpy.uint8(offspring_size[1]/2)

for k in range(offspring_size[0]):

# Index of the first parent to mate.

parent1_idx = k%parents.shape[0]

# Index of the second parent to mate.

parent2_idx = (k+1)%parents.shape[0]

# The new offspring will have its first half of its genes taken from the first parent.

offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]

# The new offspring will have its second half of its genes taken from the second parent.

offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]

return offspring

def mutation(offspring_crossover, num_mutations=1):

mutations_counter = numpy.uint8(offspring_crossover.shape[1] / num_mutations)

# Mutation changes a number of genes as defined by the num_mutations argument. The changes are random.

for idx in range(offspring_crossover.shape[0]):

gene_idx = mutations_counter - 1

for mutation_num in range(num_mutations):

# The random value to be added to the gene.

random_value = numpy.random.uniform(-1.0, 1.0, 1)

offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value

gene_idx = gene_idx + mutations_counter

return offspring_crossover