Hello World Genetic Algorithm

hello_world_genetic_algorithm.py

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import random
import numpy as np

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def calculate_fitness(individual_chromosome):
    in_sample = [4,-2,7,5,11,1]
    fitness   = 1/np.abs(44.1-np.sum(np.multiply(individual_chromosome,in_sample)))
    return fitness

def population_fitness(population):
    qualities = np.zeros(population.shape[0])
    for individual_idx in range(0, population.shape[0]):
        qualities[individual_idx] = calculate_fitness(population[individual_idx, :])
    return qualities

def select_mating_pool(population, qualities, num_parents):
    parents = np.empty((num_parents, population.shape[1]))
    for parent_idx in range(0, num_parents):
        max_quality_idx = np.where(qualities==np.max(qualities))
        max_quality_idx = max_quality_idx[0][0]
        parents[parent_idx, :] = population[max_quality_idx, :]
        qualities[max_quality_idx] = -1
    return parents

def crossover(parents, n_individuals=6):
    new_population = np.empty((6,6))
    new_population[0:parents.shape[0], :] = parents
    new_population[3,0:3] = parents[0, 0:3]
    new_population[3,3: ] = parents[1, 3: ]
    new_population[4,0:3] = parents[1, 0:3]
    new_population[4,3: ] = parents[2, 3: ]
    new_population[5,0:3] = parents[2, 0:3]
    new_population[5,3: ] = parents[0, 3: ]
    return new_population

def mutate(population):
    for idx in range(0, population.shape[0]):
        population[idx, 4] = population[idx, 4]/3
    return population


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generation_size     = 10
population_size     = 6
mating_parents_size = 3

new_population = np.zeros((6,6))
new_population[0, :] = [ 2.4, 0.7,  8.0, -2.0, 5.0,  1.1]
new_population[0, :] = [-0.4, 2.7,  5.0, -1.0, 7.0,  0.1]
new_population[0, :] = [-1.0, 2.0,  2.0, -3.0, 2.0,  0.9]
new_population[0, :] = [ 4.0, 7.0, 12.0,  6.1, 1.4, -4.0]
new_population[0, :] = [ 3.1, 4.0,  0.0,  2.4, 4.8,  0.0]
new_population[0, :] = [-2.0, 3.0, -7.0,  6.0, 3.0,  3.3]


for generation_idx in range(0, generation_size):
    qualities      = population_fitness(new_population)
    parents        = select_mating_pool(new_population, qualities, mating_parents_size)
    new_population = crossover(parents, n_individuals=population_size)
    new_population = mutate(new_population)


in_sample = [4,-2, 7, 5, 11, 1]
for k in range(6):
    print(f"{round(np.sum(np.multiply(new_population[k, :], in_sample)),1)}")


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