122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
import random
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import keyboard as keyboard
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import field as F
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from ga_methods import *
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from src import mapschema as maps
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# Genetic Algorithm
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def genetic_algorithm_setup(field):
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population_units = ["", "w", "p", "s"]
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# new_population to be
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population_text = []
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# Populate the population_text array
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for row in range(D.GSIZE):
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population_text.append([])
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for column in range(D.GSIZE):
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population_text[row].append(random.choice(population_units))
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# printer
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for _ in population_text:
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print(population_text)
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"""
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Genetic algorithm parameters:
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Mating pool size
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Population size
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"""
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# units per population in generation
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sol_per_pop = 8
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num_parents_mating = 4
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population_values = []
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fitness_row = []
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# population Fitness
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for i in range(0, D.GSIZE):
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for j in range(D.GSIZE):
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fitness_row.append(local_fitness(field, i, j, population_text))
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population_values.append(fitness_row)
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best_outputs = []
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num_generations = 10
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generation = 0
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while generation < num_generations:
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if keyboard.is_pressed('space'):
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generation += 1
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print("Generation : ", generation)
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# Measuring the fitness of each chromosome in the population.
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fitness = cal_pop_fitness(population_values)
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print("Fitness")
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print(fitness)
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# best_outputs.append(best_Output(new_population))
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# The best result in the current iteration.
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# print("Best result : ", best_Output(new_population))
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# Selecting the best parents in the population for mating.
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parents = select_mating_pool(new_population, fitness,
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num_parents_mating)
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print("Parents")
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print(parents)
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# Generating next generation using crossover.
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offspring_crossover = crossover(parents, offspring_size=(pop_size[0] - parents.shape[0], num_weights))
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print("Crossover")
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print(offspring_crossover)
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# Adding some variations to the offspring using mutation.
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offspring_mutation = mutation(offspring_crossover, num_mutations=2)
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print("Mutation")
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print(offspring_mutation)
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# Creating the new population based on the parents and offspring.
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new_population[0:parents.shape[0], :] = parents
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new_population[parents.shape[0]:, :] = offspring_mutation
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# Getting the best solution after iterating finishing all generations.
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# At first, the fitness is calculated for each solution in the final generation.
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fitness = cal_pop_fitness(new_population)
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# Then return the index of that solution corresponding to the best fitness.
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best_match_idx = numpy.where(fitness == numpy.max(fitness))
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print("Best solution : ", new_population[best_match_idx, :])
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print("Best solution fitness : ", fitness[best_match_idx])
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import matplotlib.pyplot
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matplotlib.pyplot.plot(best_outputs)
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matplotlib.pyplot.xlabel("Iteration")
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matplotlib.pyplot.ylabel("Fitness")
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matplotlib.pyplot.show()
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# return best iteration of field
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return 0
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if __name__ == "__main__":
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# Define the map of the field
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mapschema = maps.createField()
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# Create field array
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field = []
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# Populate the field array
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for row in range(D.GSIZE):
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field.append([])
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for column in range(D.GSIZE):
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fieldbit = F.Field(row, column, mapschema[column][row])
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field[row].append(fieldbit)
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genetic_algorithm_setup(field)
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