def run(self):
time_start = time.time()
next_generation_individuals = np.array([False])
for generation in range(self.epochs_num):
new_population = Population(self.chromosome_type, self.population_size, self.chromosomes_number,
self.range_start, self.range_end, self.accuracy, self.fitness_function,
self.searching_value, self.crossover_type, self.crossover_prob,
self.elite_strategy_num,
next_generation_individuals)
self.population_set.add(new_population)
if self.elite_strategy_num > 0:
self.best_individuals = self.elite_strategy(new_population.best_individuals)
new_population.select_individuals(self.selection_type, self.selection_args)
new_individuals = new_population.crossover_selected_individuals()
if self.mutation_prob > 0.0:
Population.mutate_individuals(new_individuals, self.mutation_type, self.mutation_prob)
if self.inversion_prob > 0.0:
Population.inverse_individuals(new_individuals, self.inversion_prob)
if self.elite_strategy_num > 0:
next_generation_individuals = np.append(new_individuals, self.best_individuals)
else:
next_generation_individuals = new_individuals
self.fill_values_for_charts(next_generation_individuals, generation)
best = Population.get_n_best_individuals(1, self.searching_value, next_generation_individuals,
self.fitness_function)
self.best_individual = best[0]
time_end = time.time()
self.time = time_end - time_start
print("Final best:")
print(best[0].get_decimal_value_of_chromosomes())
print("value:")
print(best[0].evaluate(self.fitness_function))
print("evolution time: ")
print(self.time)
print("BESTS")
print(self.bests_values)
print("MEAN")
print(self.mean_values)
print("STD")
print(self.sd_values)
def elite_strategy(self, new_best_candidates):
individuals = np.asarray(list(set(np.append(self.best_individuals, new_best_candidates))))
return Population.get_n_best_individuals(self.elite_strategy_num, self.searching_value, individuals,
self.fitness_function)