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Update previous_generation_fitness once GA completes
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@ahmedfgad
ahmedfgad committed Jan 7, 2025
1 parent 58a2ecb commit 13e5713
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Showing 2 changed files with 108 additions and 81 deletions.
82 changes: 47 additions & 35 deletions pygad/pygad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1211,7 +1211,7 @@ def __init__(self,
self.valid_parameters = False
raise ValueError(f"In the 'stop_criteria' parameter, the supported stop words are {self.supported_stop_words} but '{stop_word}' found.")

if number.replace(".", "").isnumeric():
if number.replace(".", "").replace("-", "").isnumeric():
number = float(number)
else:
self.valid_parameters = False
Expand Down Expand Up @@ -1306,6 +1306,8 @@ def __init__(self,
# A list holding the offspring after applying mutation in the last generation.
self.last_generation_offspring_mutation = None
# Holds the fitness values of one generation before the fitness values saved in the last_generation_fitness attribute. Added in PyGAD 2.16.2.
# They are used inside the cal_pop_fitness() method to fetch the fitness of the parents in one generation before the latest generation.
# This is to avoid re-calculating the fitness for such parents again.
self.previous_generation_fitness = None
# Added in PyGAD 2.18.0. A NumPy array holding the elitism of the current generation according to the value passed in the 'keep_elitism' parameter. It works only if the 'keep_elitism' parameter has a non-zero value.
self.last_generation_elitism = None
Expand Down Expand Up @@ -1640,14 +1642,12 @@ def cal_pop_fitness(self):
# 'last_generation_parents_as_list' is the list version of 'self.last_generation_parents'
# It is used to return the parent index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'.
if self.last_generation_parents is not None:
last_generation_parents_as_list = [
list(gen_parent) for gen_parent in self.last_generation_parents]
last_generation_parents_as_list = self.last_generation_parents.tolist()

# 'last_generation_elitism_as_list' is the list version of 'self.last_generation_elitism'
# It is used to return the elitism index using the 'in' membership operator of Python lists. This is much faster than using 'numpy.where()'.
if self.last_generation_elitism is not None:
last_generation_elitism_as_list = [
list(gen_elitism) for gen_elitism in self.last_generation_elitism]
last_generation_elitism_as_list = self.last_generation_elitism.tolist()

pop_fitness = ["undefined"] * len(self.population)
if self.parallel_processing is None:
Expand All @@ -1659,6 +1659,12 @@ def cal_pop_fitness(self):
# Make sure that both the solution and 'self.solutions' are of type 'list' not 'numpy.ndarray'.
# if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(self.solutions == numpy.array(sol), axis=1)))
# if (self.save_solutions) and (len(self.solutions) > 0) and (numpy.any(numpy.all(numpy.equal(self.solutions, numpy.array(sol)), axis=1)))

# Make sure self.best_solutions is a list of lists before proceeding.
# Because the second condition expects that best_solutions is a list of lists.
if type(self.best_solutions) is numpy.ndarray:
self.best_solutions = self.best_solutions.tolist()

if (self.save_solutions) and (len(self.solutions) > 0) and (list(sol) in self.solutions):
solution_idx = self.solutions.index(list(sol))
fitness = self.solutions_fitness[solution_idx]
Expand Down Expand Up @@ -1867,13 +1873,13 @@ def run(self):

# self.best_solutions: Holds the best solution in each generation.
if type(self.best_solutions) is numpy.ndarray:
self.best_solutions = list(self.best_solutions)
self.best_solutions = self.best_solutions.tolist()
# self.best_solutions_fitness: A list holding the fitness value of the best solution for each generation.
if type(self.best_solutions_fitness) is numpy.ndarray:
self.best_solutions_fitness = list(self.best_solutions_fitness)
# self.solutions: Holds the solutions in each generation.
if type(self.solutions) is numpy.ndarray:
self.solutions = list(self.solutions)
self.solutions = self.solutions.tolist()
# self.solutions_fitness: Holds the fitness of the solutions in each generation.
if type(self.solutions_fitness) is numpy.ndarray:
self.solutions_fitness = list(self.solutions_fitness)
Expand Down Expand Up @@ -1913,34 +1919,8 @@ def run(self):
self.best_solutions.append(list(best_solution))

for generation in range(generation_first_idx, generation_last_idx):
if not (self.on_fitness is None):
on_fitness_output = self.on_fitness(self,
self.last_generation_fitness)

if on_fitness_output is None:
pass
else:
if type(on_fitness_output) in [tuple, list, numpy.ndarray, range]:
on_fitness_output = numpy.array(on_fitness_output)
if on_fitness_output.shape == self.last_generation_fitness.shape:
self.last_generation_fitness = on_fitness_output
else:
raise ValueError(f"Size mismatch between the output of on_fitness() {on_fitness_output.shape} and the expected fitness output {self.last_generation_fitness.shape}.")
else:
raise ValueError(f"The output of on_fitness() is expected to be tuple/list/range/numpy.ndarray but {type(on_fitness_output)} found.")

# Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute.
self.best_solutions_fitness.append(best_solution_fitness)

# Appending the solutions in the current generation to the solutions list.
if self.save_solutions:
# self.solutions.extend(self.population.copy())
population_as_list = self.population.copy()
population_as_list = [list(item)
for item in population_as_list]
self.solutions.extend(population_as_list)

self.solutions_fitness.extend(self.last_generation_fitness)
self.run_loop_head(best_solution_fitness)

# Call the 'run_select_parents()' method to select the parents.
# It edits these 2 instance attributes:
Expand All @@ -1964,7 +1944,6 @@ def run(self):
# 1) population: A NumPy array of the population of solutions/chromosomes.
self.run_update_population()


# The generations_completed attribute holds the number of the last completed generation.
self.generations_completed = generation + 1

Expand Down Expand Up @@ -2078,13 +2057,45 @@ def run(self):
# Converting the 'best_solutions' list into a NumPy array.
self.best_solutions = numpy.array(self.best_solutions)

# Update previous_generation_fitness because it is used to get the fitness of the parents.
self.previous_generation_fitness = self.last_generation_fitness.copy()

# Converting the 'solutions' list into a NumPy array.
# self.solutions = numpy.array(self.solutions)
except Exception as ex:
self.logger.exception(ex)
# sys.exit(-1)
raise ex

def run_loop_head(self, best_solution_fitness):
if not (self.on_fitness is None):
on_fitness_output = self.on_fitness(self,
self.last_generation_fitness)

if on_fitness_output is None:
pass
else:
if type(on_fitness_output) in [tuple, list, numpy.ndarray, range]:
on_fitness_output = numpy.array(on_fitness_output)
if on_fitness_output.shape == self.last_generation_fitness.shape:
self.last_generation_fitness = on_fitness_output
else:
raise ValueError(f"Size mismatch between the output of on_fitness() {on_fitness_output.shape} and the expected fitness output {self.last_generation_fitness.shape}.")
else:
raise ValueError(f"The output of on_fitness() is expected to be tuple/list/range/numpy.ndarray but {type(on_fitness_output)} found.")

# Appending the fitness value of the best solution in the current generation to the best_solutions_fitness attribute.
self.best_solutions_fitness.append(best_solution_fitness)

# Appending the solutions in the current generation to the solutions list.
if self.save_solutions:
# self.solutions.extend(self.population.copy())
population_as_list = self.population.copy()
population_as_list = [list(item) for item in population_as_list]
self.solutions.extend(population_as_list)

self.solutions_fitness.extend(self.last_generation_fitness)

def run_select_parents(self, call_on_parents=True):
"""
This method must be only callled from inside the run() method. It is not meant for use by the user.
Expand Down Expand Up @@ -2333,6 +2344,7 @@ def best_solution(self, pop_fitness=None):
-best_solution_fitness: Fitness value of the best solution.
-best_match_idx: Index of the best solution in the current population.
"""

try:
if pop_fitness is None:
# If the 'pop_fitness' parameter is not passed, then we have to call the 'cal_pop_fitness()' method to calculate the fitness of all solutions in the lastest population.
Expand Down
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