For explanation see: 94: Genetic algorithms 1. A simple genetic algorithm

import random import numpy as np def create_reference_solution(chromosome_length): number_of_ones = int(chromosome_length / 2) # Build an array with an equal mix of zero and ones reference = np.zeros(chromosome_length) reference[0: number_of_ones] = 1 # Shuffle the array to mix the zeros and ones np.random.shuffle(reference) return reference def create_starting_population(individuals, chromosome_length): # Set up an initial array of all zeros population = np.zeros((individuals, chromosome_length)) # Loop through each row (individual) for i in range(individuals): # Choose a random number of ones to create ones = random.randint(0, chromosome_length) # Change the required number of zeros to ones population[i, 0:ones] = 1 # Sfuffle row np.random.shuffle(population[i]) return population def calculate_fitness(reference, population): # Create an array of True/False compared to reference identical_to_reference = population == reference # Sum number of genes that are identical to the reference fitness_scores = identical_to_reference.sum(axis=1) return fitness_scores def select_individual_by_tournament(population, scores): # Get population size population_size = len(scores) # Pick individuals for tournament fighter_1 = random.randint(0, population_size-1) fighter_2 = random.randint(0, population_size-1) # Get fitness score for each fighter_1_fitness = scores[fighter_1] fighter_2_fitness = scores[fighter_2] # Identify undividual with highest fitness # Fighter 1 will win if score are equal if fighter_1_fitness >= fighter_2_fitness: winner = fighter_1 else: winner = fighter_2 # Return the chromsome of the winner return population[winner, :] def breed_by_crossover(parent_1, parent_2): # Get length of chromosome chromosome_length = len(parent_1) # Pick crossover point, avoding ends of chromsome crossover_point = random.randint(1,chromosome_length-1) # Create children. np.hstack joins two arrays child_1 = np.hstack((parent_1[0:crossover_point], parent_2[crossover_point:])) child_2 = np.hstack((parent_2[0:crossover_point], parent_1[crossover_point:])) # Return children return child_1, child_2 def randomly_mutate_population(population, mutation_probability): # Apply random mutation random_mutation_array = np.random.random( size=(population.shape)) random_mutation_boolean = \ random_mutation_array <= mutation_probability population[random_mutation_boolean] = \ np.logical_not(population[random_mutation_boolean]) # Return mutation population return population # ************************************* # ******** MAIN ALGORITHM CODE ******** # ************************************* # Set general parameters chromosome_length = 75 population_size = 500 maximum_generation = 200 best_score_progress = [] # Tracks progress # Create reference solution # (this is used just to illustrate GAs) reference = create_reference_solution(chromosome_length) # Create starting population population = create_starting_population(population_size, chromosome_length) # Display best score in starting population scores = calculate_fitness(reference, population) best_score = np.max(scores)/chromosome_length * 100 print ('Starting best score, % target: ',best_score) # Add starting best score to progress tracker best_score_progress.append(best_score) # Now we'll go through the generations of genetic algorithm for generation in range(maximum_generation): # Create an empty list for new population new_population = [] # Create new popualtion generating two children at a time for i in range(int(population_size/2)): parent_1 = select_individual_by_tournament(population, scores) parent_2 = select_individual_by_tournament(population, scores) child_1, child_2 = breed_by_crossover(parent_1, parent_2) new_population.append(child_1) new_population.append(child_2) # Replace the old population with the new one population = np.array(new_population) # Apply mutation mutation_rate = 0.002 population = randomly_mutate_population(population, mutation_rate) # Score best solution, and add to tracker scores = calculate_fitness(reference, population) best_score = np.max(scores)/chromosome_length * 100 best_score_progress.append(best_score) # GA has completed required generation print ('End best score, % target: ', best_score) # Plot progress import matplotlib.pyplot as plt plt.plot(best_score_progress) plt.xlabel('Generation') plt.ylabel('Best score (% target)') plt.show()

Where is randomly_mutate_population(population, mutation_probability) using in main algorythm?

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Well spotted! It is now added. You usually do it after creating the new child population.

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