import copy import random import matplotlib import matplotlib.pyplot import numpy import src.dimensions as D # Genetic Algorithm methods def local_fitness(field, x, y, plants_case): soil_value = 0 if field[x][y].field_type == "soil": soil_value = 1 else: soil_value = 0.5 if plants_case[x][y] == "": plant_value = 0 elif plants_case[x][y] == "w": plant_value = 1 elif plants_case[x][y] == "p": plant_value = 2 elif plants_case[x][y] == "s": plant_value = 3 else: plant_value = 1 neighbour_bonus = 1 if x - 1 >= 0: if plants_case[x][y] == plants_case[x - 1][y]: neighbour_bonus += 1 if x + 1 < D.GSIZE: if plants_case[x][y] == plants_case[x + 1][y]: neighbour_bonus += 1 if y - 1 >= 0: if plants_case[x][y] == plants_case[x][y - 1]: neighbour_bonus += 1 if y + 1 < D.GSIZE: if plants_case[x][y] == plants_case[x][y + 1]: neighbour_bonus += 1 local_fitness_value = (soil_value + plant_value) * (0.5 * neighbour_bonus + 1) return local_fitness_value def population_fitness(population_text_local, field, population_size): # 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 = [] for k in range(population_size): population_values_single = [] population_values_single_row = [] fitness_row = [] for i in range(0, D.GSIZE): for j in range(0, D.GSIZE): population_values_single_row.append(local_fitness(field, i, j, population_text_local)) population_values_single.append(population_values_single_row) for i in range(D.GSIZE): fitness_row.append(sum(population_values_single[i])) fitness = sum(fitness_row) return fitness def crossover(local_parents): ret = [] for i in range(0, len(local_parents)): child = copy.deepcopy(local_parents[i]) # Vertical randomization width = random.randint(1, D.GSIZE // len(local_parents)) # width of stripes indexes_parents = numpy.random.permutation(range(0, len(local_parents))) # sorting of stripes beginning = random.randint(0, len(local_parents[0]) - width * len( local_parents)) # point we start putting the stripes from for x in indexes_parents: child[beginning:beginning + width] = local_parents[x][beginning:beginning + width] beginning += width ret.append(child) return ret def mutation(population_units, offspring_crossover, num_mutants, num_mutations=10): for case in range(0, len(offspring_crossover)): for mutation in range(0, num_mutations): mutation_x = random.randint(0, D.GSIZE - 1) mutation_y = random.randint(0, D.GSIZE - 1) mutation_value = random.choice(population_units) offspring_crossover[case][mutation_x][mutation_y] = mutation_value num_mutants -= 1 return offspring_crossover def pretty_printer(best_outputs): matplotlib.pyplot.plot(best_outputs) matplotlib.pyplot.xlabel("Iteration") matplotlib.pyplot.ylabel("Fitness") matplotlib.pyplot.show()