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PyGAD 3.3.1
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Release Date 17 February 2024
1. After the last generation and before the `run()` method completes, update the 2 instance attributes: 1) `last_generation_parents` 2) `last_generation_parents_indices`. This is to keep the list of parents up-to-date with the latest population fitness `last_generation_fitness`.  #275
2. 4 methods with names starting with `run_`. Their purpose is to keep the main loop inside the `run()` method clean. Check the [Other Methods](https://pygad.readthedocs.io/en/latest/pygad.html#other-methods) section for more information.
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ahmedfgad committed Feb 17, 2024
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8 changes: 6 additions & 2 deletions docs/source/pygad.rst
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Expand Up @@ -610,8 +610,12 @@ Other Methods
from inside the ``run()`` method. Supported in `PyGAD
3.3.1 <https://pygad.readthedocs.io/en/latest/releases.html#pygad-3-3-1>`__.

1. ``run_select_parents()``: Select the parents and call the callable
``on_parents()`` if defined.
1. ``run_select_parents(call_on_parents=True)``: Select the parents
and call the callable ``on_parents()`` if defined. If
``call_on_parents`` is ``True``, then the callable
``on_parents()`` is called. It must be ``False`` when the
``run_select_parents()`` method is called to update the parents at
the end of the ``run()`` method.

2. ``run_crossover()``: Apply crossover and call the callable
``on_crossover()`` if defined.
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19 changes: 19 additions & 0 deletions docs/source/releases.rst
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Expand Up @@ -1532,6 +1532,25 @@ Release Date 29 January 2024
self.best_solution_generation = numpy.where(numpy.array(
self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0]
.. _pygad-331:

PyGAD 3.3.1
-----------

Release Date 17 February 2024

1. After the last generation and before the ``run()`` method completes,
update the 2 instance attributes: 1) ``last_generation_parents`` 2)
``last_generation_parents_indices``. This is to keep the list of
parents up-to-date with the latest population fitness
``last_generation_fitness``.
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/275

2. 4 methods with names starting with ``run_``. Their purpose is to keep
the main loop inside the ``run()`` method clean. Check the `Other
Methods <https://pygad.readthedocs.io/en/latest/pygad.html#other-methods>`__
section for more information.

PyGAD Projects at GitHub
========================

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85 changes: 46 additions & 39 deletions pygad/pygad.py
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Expand Up @@ -2067,7 +2067,8 @@ def run(self):

# Call the run_select_parents() method to update these 2 attributes according to the 'last_generation_fitness' attribute:
# 1) last_generation_parents 2) last_generation_parents_indices
self.run_select_parents()
# Set 'call_on_parents=False' to avoid calling the callable 'on_parents' because this step is not part of the cycle.
self.run_select_parents(call_on_parents=False)

# Save the fitness value of the best solution.
_, best_solution_fitness, _ = self.best_solution(
Expand All @@ -2093,7 +2094,7 @@ def run(self):
# sys.exit(-1)
raise ex

def run_select_parents(self):
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.
Generally, any method with a name starting with 'run_' is meant to be only called by PyGAD from inside the 'run()' method.
Expand All @@ -2103,6 +2104,11 @@ def run_select_parents(self):
1) last_generation_parents: A NumPy array of the selected parents.
2) last_generation_parents_indices: A 1D NumPy array of the indices of the selected parents.
Parameters
----------
call_on_parents : bool, optional
If True, then the callable 'on_parents()' is called. The default is True.
Returns
-------
None.
Expand Down Expand Up @@ -2133,46 +2139,47 @@ def run_select_parents(self):
elif len(self.last_generation_parents_indices) != self.num_parents_mating:
raise ValueError(f"The iterable holding the selected parents indices is expected to have ({self.num_parents_mating}) values but ({len(self.last_generation_parents_indices)}) found.")

if not (self.on_parents is None):
on_parents_output = self.on_parents(self,
self.last_generation_parents)

if on_parents_output is None:
pass
elif type(on_parents_output) in [list, tuple, numpy.ndarray]:
if len(on_parents_output) == 2:
on_parents_selected_parents, on_parents_selected_parents_indices = on_parents_output
else:
raise ValueError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray of length 2 but {type(on_parents_output)} of length {len(on_parents_output)} found.")

# Validate the parents.
if on_parents_selected_parents is None:
raise ValueError("The returned outputs of on_parents() cannot be None but the first output is None.")
else:
if type(on_parents_selected_parents) in [tuple, list, numpy.ndarray]:
on_parents_selected_parents = numpy.array(on_parents_selected_parents)
if on_parents_selected_parents.shape == self.last_generation_parents.shape:
self.last_generation_parents = on_parents_selected_parents
else:
raise ValueError(f"Size mismatch between the parents retrned by on_parents() {on_parents_selected_parents.shape} and the expected parents shape {self.last_generation_parents.shape}.")
if call_on_parents:
if not (self.on_parents is None):
on_parents_output = self.on_parents(self,
self.last_generation_parents)

if on_parents_output is None:
pass
elif type(on_parents_output) in [list, tuple, numpy.ndarray]:
if len(on_parents_output) == 2:
on_parents_selected_parents, on_parents_selected_parents_indices = on_parents_output
else:
raise ValueError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray but the first output type is {type(on_parents_selected_parents)}.")

# Validate the parents indices.
if on_parents_selected_parents_indices is None:
raise ValueError("The returned outputs of on_parents() cannot be None but the second output is None.")
else:
if type(on_parents_selected_parents_indices) in [tuple, list, numpy.ndarray, range]:
on_parents_selected_parents_indices = numpy.array(on_parents_selected_parents_indices)
if on_parents_selected_parents_indices.shape == self.last_generation_parents_indices.shape:
self.last_generation_parents_indices = on_parents_selected_parents_indices
raise ValueError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray of length 2 but {type(on_parents_output)} of length {len(on_parents_output)} found.")

# Validate the parents.
if on_parents_selected_parents is None:
raise ValueError("The returned outputs of on_parents() cannot be None but the first output is None.")
else:
if type(on_parents_selected_parents) in [tuple, list, numpy.ndarray]:
on_parents_selected_parents = numpy.array(on_parents_selected_parents)
if on_parents_selected_parents.shape == self.last_generation_parents.shape:
self.last_generation_parents = on_parents_selected_parents
else:
raise ValueError(f"Size mismatch between the parents retrned by on_parents() {on_parents_selected_parents.shape} and the expected parents shape {self.last_generation_parents.shape}.")
else:
raise ValueError(f"Size mismatch between the parents indices returned by on_parents() {on_parents_selected_parents_indices.shape} and the expected crossover output {self.last_generation_parents_indices.shape}.")
raise ValueError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray but the first output type is {type(on_parents_selected_parents)}.")

# Validate the parents indices.
if on_parents_selected_parents_indices is None:
raise ValueError("The returned outputs of on_parents() cannot be None but the second output is None.")
else:
raise ValueError(f"The output of on_parents() is expected to be tuple/list/range/numpy.ndarray but the second output type is {type(on_parents_selected_parents_indices)}.")

else:
raise TypeError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray but {type(on_parents_output)} found.")
if type(on_parents_selected_parents_indices) in [tuple, list, numpy.ndarray, range]:
on_parents_selected_parents_indices = numpy.array(on_parents_selected_parents_indices)
if on_parents_selected_parents_indices.shape == self.last_generation_parents_indices.shape:
self.last_generation_parents_indices = on_parents_selected_parents_indices
else:
raise ValueError(f"Size mismatch between the parents indices returned by on_parents() {on_parents_selected_parents_indices.shape} and the expected crossover output {self.last_generation_parents_indices.shape}.")
else:
raise ValueError(f"The output of on_parents() is expected to be tuple/list/range/numpy.ndarray but the second output type is {type(on_parents_selected_parents_indices)}.")

else:
raise TypeError(f"The output of on_parents() is expected to be tuple/list/numpy.ndarray but {type(on_parents_output)} found.")

def run_crossover(self):
"""
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