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Commit 19bac06

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PyGAD 2.16.1
1. Reuse the fitness of previously explored solutions rather than recalculating them. This feature only works if `save_solutions=True`. 2. The user can use the `tqdm` library to show a progress bar. #50 ```python import pygad import numpy import tqdm equation_inputs = [4,-2,3.5] desired_output = 44 def fitness_func(solution, solution_idx): output = numpy.sum(solution * equation_inputs) fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001) return fitness num_generations = 10000 with tqdm.tqdm(total=num_generations) as pbar: ga_instance = pygad.GA(num_generations=num_generations, sol_per_pop=5, num_parents_mating=2, num_genes=len(equation_inputs), fitness_func=fitness_func, on_generation=lambda _: pbar.update(1)) ga_instance.run() ga_instance.plot_result() ``` 3. Solved the issue of unequal length between the `solutions` and `solutions_fitness` when the `save_solutions` parameter is set to `True`. Now, the fitness of the last population is appended to the `solutions_fitness` array. #64 4. There was an issue of getting the length of these 4 variables (`solutions`, `solutions_fitness`, `best_solutions`, and `best_solutions_fitness`) doubled after each call of the `run()` method. This is solved by resetting these variables at the beginning of the `run()` method. #62 5. Bug fixes when adaptive mutation is used (`mutation_type="adaptive"`). #65 
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‎__init__.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,3 @@
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from .pygad import * # Relative import.
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3-
__version__ = "2.16.0"
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__version__ = "2.16.1"

‎pygad.py

Lines changed: 29 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -1169,7 +1169,14 @@ def run(self):
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"""
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if self.valid_parameters == False:
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raise ValueError("ERROR calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n")
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raise ValueError("Error calling the run() method: \nThe run() method cannot be executed with invalid parameters. Please check the parameters passed while creating an instance of the GA class.\n")
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# Reset the variables that store the solutions and their fitness after each generation. If not reset, then for each call to the run() method the new solutions and their fitness values will be appended to the old variables and their length double. Some errors arise if not reset.
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# If, in the future, new variables are created that get appended after each generation, please consider resetting them here.
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self.best_solutions = [] # Holds the best solution in each generation.
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self.best_solutions_fitness = [] # A list holding the fitness value of the best solution for each generation.
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self.solutions = [] # Holds the solutions in each generation.
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self.solutions_fitness = [] # Holds the fitness of the solutions in each generation.
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if not (self.on_start is None):
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self.on_start(self)
@@ -1290,6 +1297,10 @@ def run(self):
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time.sleep(self.delay_after_gen)
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# Save the fitness of the last generation.
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if self.save_solutions:
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self.solutions_fitness.extend(self.last_generation_fitness)
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# Save the fitness value of the best solution.
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_, best_solution_fitness, _ = self.best_solution(pop_fitness=self.last_generation_fitness)
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self.best_solutions_fitness.append(best_solution_fitness)
@@ -1316,7 +1327,7 @@ def steady_state_selection(self, fitness, num_parents):
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-num_parents: The number of parents to be selected.
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It returns an array of the selected parents.
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"""
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fitness_sorted = sorted(range(len(fitness)), key=lambda k: fitness[k])
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fitness_sorted.reverse()
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# Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
@@ -2075,16 +2086,25 @@ def adaptive_mutation_population_fitness(self, offspring):
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fitness = self.last_generation_fitness.copy()
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temp_population = numpy.zeros_like(self.population)
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temp_population[0:self.last_generation_parents.shape[0], :] = self.last_generation_parents.copy()
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temp_population[self.last_generation_parents.shape[0]:, :] = offspring
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if (self.keep_parents == 0):
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parents_to_keep = []
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elif (self.keep_parents == -1):
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parents_to_keep = self.last_generation_parents.copy()
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temp_population[0:len(parents_to_keep), :] = parents_to_keep
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elif (self.keep_parents > 0):
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parents_to_keep, _ = self.steady_state_selection(self.last_generation_fitness, num_parents=self.keep_parents)
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temp_population[0:len(parents_to_keep), :] = parents_to_keep
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temp_population[len(parents_to_keep):, :] = offspring
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fitness[:self.last_generation_parents.shape[0]] = self.last_generation_fitness[self.last_generation_parents_indices]
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for idx in range(self.last_generation_parents.shape[0], fitness.shape[0]):
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for idx in range(len(parents_to_keep), fitness.shape[0]):
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fitness[idx] = self.fitness_func(temp_population[idx], None)
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average_fitness = numpy.mean(fitness)
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return average_fitness, fitness[self.last_generation_parents.shape[0]:]
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return average_fitness, fitness[len(parents_to_keep):]
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def adaptive_mutation(self, offspring):
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@@ -3433,7 +3453,7 @@ def plot_genes(self,
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bbox_inches='tight')
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matplotlib.pyplot.show()
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return fig
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def save(self, filename):
@@ -3460,5 +3480,5 @@ def load(filename):
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except FileNotFoundError:
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raise FileNotFoundError("Error reading the file {filename}. Please check your inputs.".format(filename=filename))
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except:
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raise BaseException("Error loading the file. Please check if the file exists.")
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raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).")
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return ga_in

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