GA.py

import numpy as np


def cal_pop_fitness(fitness_func, pop):
    fitness = fitness_func(pop)
    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 = np.empty((num_parents, pop.shape[1]))
    for parent_num in range(num_parents):
        max_fitness_idx = np.where(fitness == np.min(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 = np.empty(offspring_size)
    # The point at which crossover takes place between two parents. Usually it is at the center.
    crossover_point = np.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):
    # Mutation changes a single gene in each offspring randomly.
    for idx in range(offspring_crossover.shape[0]):
        # The random value to be added to the gene.
        random_value = np.random.uniform(0, 1, 1)
        offspring_crossover[idx-1, offspring_crossover.shape[0]-1] = \
            offspring_crossover[idx-1, offspring_crossover.shape[0]-1] + random_value
    return offspring_crossover