""" Program that optimizes Rastrigin function: file_ (x_point_value, y_point_value) = 20 + (x_point_value^2 - 10cos(2πx)) + (y_point_value^2 - 10 cos(2πy)). Using Evolutionary Strategy (μ, λ). """ import sys import os import time import tempfile # run pylint with: # pylint --generated-members=cv2.* .\main.py import cv2 import matplotlib.pyplot as plt import numpy as np def rastrigin(x_argument, y_argument): """ Define the Rastrigin function """ return 20 + x_argument**2 - 10 * np.cos(2 * np.pi * x_argument) + \ y_argument**2 - 10 * np.cos(2 * np.pi * y_argument) def generate(population, number_of_parents=5, size_of_population=20, mutation_strength=0.1, ): """ Run single generation """ # Evaluate the fitness of each individual fitness = np.array([rastrigin(x_point_value, y_point_value) for x_point_value, y_point_value in population]) # Select the top number_of_parents individuals parents = population[np.argsort(fitness)[:number_of_parents]] # Generate the next generation of lambda individuals by recombination children = np.concatenate( [np.random.permutation(parents) for i in range((size_of_population//number_of_parents)+1)]) children = children[:size_of_population] # Add mutation to the children mutation = np.random.normal( loc=0, scale=mutation_strength, size=( size_of_population, 2)) population = children + mutation return fitness, population def evolution_strategy( number_of_parents=5, size_of_population=20, mutation_strength=0.1, number_of_generations=123, min_max=(-5.12, 5.12), number_of_outputs = 10 ): """ Define the Evolutionary Strategy (μ, λ) algorithm """ # Initialize the population population = np.random.uniform( low=min_max[0], high=min_max[1], size=( size_of_population, 2)) summary = [] output(population, 0, f"0&nop_{number_of_parents}&sop_{size_of_population}&ms_{mutation_strength}&nog_{number_of_generations}&min_max_{min_max}&noo_{number_of_outputs}") number_of_outputs = min([number_of_outputs-1, number_of_generations]) # Iterate until we reach max number of generate and terminate for generation_number in range(1, number_of_generations+1): fitness, population = generate( population, number_of_parents, size_of_population, mutation_strength) step = number_of_generations//number_of_outputs \ if number_of_generations % number_of_outputs == 0 \ else number_of_generations//(number_of_outputs-1) offset = number_of_generations % step if (generation_number - offset) % step == 0: output(population, generation_number, f"{generation_number}&nop_{number_of_parents}&sop_{size_of_population}&ms_{mutation_strength}&nog_{number_of_generations}&min_max_{min_max}&noo_{number_of_outputs}") summary.append(population) print_summary(summary, f"{generation_number}&nop_{number_of_parents}&sop_{size_of_population}&ms_{mutation_strength}&nog_{number_of_generations}&min_max_{min_max}&noo_{number_of_outputs}") # Evaluate the fitness of the final population fitness = np.array([rastrigin(x_point_value, y_point_value) for x_point_value, y_point_value in population]) # Return the best individual found best_idx = np.argmin(fitness) return population[best_idx], fitness[best_idx], population def print_help(): """ Print program functionality and how to access it """ print(""" python main.py - Default functionality optimizing Rastrigin function file_ (x_point_value, y_point_value) = 20 + (x_point_value^2 - 10cos(2πx)) + (y_point_value^2 - 10 cos(2πy)) using Evolutionary Strategy (μ, λ), using only default values Default values: number_of_parents=5, size_of_population=20, mutation_strength=0.1, number_of_generations=100, min_value=-5.12, max_value=5.12 number_of_outputs = 100 python main.py -h --help print this prompt Any of the default values an be changed using arguments: -nop --number_of_parents [number] -sop --size_of_population [number] -ms --mutation_strength [number] -nog --number_of_generations [number] -min --min_value [number] -max --max_value [number] -noo, --number_of_outputs [number] Those arguments can be given in any order and any argument which was not entered will be replaced with default value, exemplary use: python main.py -nop 5 -sop 20 -ms 0.1 -i 100 -min -5.12 -max 5.12 -noo 100 """) def get_output_bounds(x_data, y_data): """Get x and y output limits for pyplot""" # min_size = 0.2 min_output_size = ARGUMENTS["mutation_strength"]*10 xmin = min(x_data) xmax = max(x_data) ymin = min(y_data) ymax = max(y_data) x_diff = xmax - xmin y_diff = ymax - ymin if min_output_size is None: min_output_size = max(x_diff, y_diff) margin = max(x_diff, y_diff)/5 if x_diff < min_output_size: xmax += (min_output_size - x_diff)/2 xmin -= (min_output_size - x_diff)/2 if y_diff < min_output_size: ymax += (min_output_size - y_diff)/2 ymin -= (min_output_size - y_diff)/2 x_bounds = [xmin-margin, xmax+margin] y_bounds = [ymin-margin, ymax+margin] return x_bounds, y_bounds def output(population_output, generation_number, file_name = "temp"): """ Draw result of our function """ # define the visualization params colors = np.random.rand(len(population_output)) with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as file_: # iterate over the optimization steps x_data = [] y_data = [] for x_point_value, y_point_value in population_output: x_data.append(x_point_value) y_data.append(y_point_value) x_lim, y_lim = get_output_bounds(x_data, y_data) # plot the data plt.cla() plt.figure() plt.scatter(x_data, y_data, c=colors, alpha=0.5) plt.xlim(x_lim) plt.ylim(y_lim) plt.savefig(file_.name) # read image image = cv2.imread(file_.name) # show the image, provide window name first cv2.imshow(f"Generation {generation_number}", image) cv2.imwrite(file_name + ".jpg", image) # add wait key. window waits until user presses a key and quits if # the key is 'q' if cv2.waitKey(0) == 113: # and finally destroy/close all open windows sys.exit() cv2.destroyAllWindows() file_.close() os.unlink(file_.name) def print_summary(populations, file_name = "temp_summary"): """ Draw result of our function for chosen generations """ # define the visualization params main_color = [[1, 1, 1]] * len(populations[0]) final_color = [[0, 1, 0]] * len(populations[0]) with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as file_: # iterate over the optimization steps # generate random 2D data - replace it with the results from your # algorithm plt.cla() plt.figure() bounds = None for ind, pop in enumerate(populations): x_data = [] y_data = [] for x_point_value, y_point_value in pop: x_data.append(x_point_value) y_data.append(y_point_value) if ind == 0: bounds = get_output_bounds(x_data, y_data) # plot the data transparency = ind/(len(populations)-1) color = [[transparency, 0, 0]] * len(pop) plt.scatter(x_data, y_data, c=color, alpha=transparency, label=f"{ind}") plt.xlim(bounds[0]) plt.ylim(bounds[1]) plt.savefig(file_.name) # read image image = cv2.imread(file_.name) cv2.imwrite("SUMMARY&" + file_name + ".jpg", image) # show the image, provide window name first cv2.imshow(f"Summary", image) # add wait key. window waits until user presses a key and quits if # the key is 'q' if cv2.waitKey(0) == 113: # and finally destroy/close all open windows sys.exit() cv2.destroyAllWindows() file_.close() os.unlink(file_.name) def user_input(): """ Handle user terminal arguments""" arguments = { "number_of_parents": 5, "size_of_population": 20, "mutation_strength": 0.1, "number_of_generations": 100, "min": -5.12, "max": 5.12, "number_of_outputs": 10} for index, argument in enumerate(sys.argv): if argument in ('-h', '--help'): print_help() sys.exit() if argument in ('-nop', '--number_of_parents'): arguments["number_of_parents"] = int(sys.argv[index+1]) if argument in ('-sop', '--size_of_population'): arguments["size_of_population"] = int(sys.argv[index+1]) if argument in ('-ms', '--mutation_strength'): arguments["mutation_strength"] = float(sys.argv[index+1]) if argument in ('-nog', '--number_of_generations'): arguments["number_of_generations"] = int(sys.argv[index+1]) if argument in ('-min', '--min_value'): arguments["min"] = float(sys.argv[index+1]) if argument in ('-max', '--max_value'): arguments["max"] = float(sys.argv[index+1]) if argument in ('-noo', '--number_of_outputs'): arguments["number_of_outputs"] = int(sys.argv[index + 1]) return arguments # Ran first in the code if __name__ == "__main__": # Run the Evolutionary Strategy algorithm ARGUMENTS = user_input() start_time = time.perf_counter() best_individual, best_fitness, output_population = evolution_strategy( ARGUMENTS["number_of_parents"], ARGUMENTS["size_of_population"], ARGUMENTS["mutation_strength"], ARGUMENTS["number_of_generations"], (ARGUMENTS["min"], ARGUMENTS["max"]), ARGUMENTS["number_of_outputs"]) end_time = time.perf_counter() total_generation_time = end_time - start_time time_per_generation = total_generation_time / \ ARGUMENTS["number_of_generations"] print("Best individual found:", best_individual) print("Best fitness found:", best_fitness) print("total_generation_time: ", total_generation_time) print("time_per_generation: ", time_per_generation)