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Commit 9ac7527

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Merge pull request #334 from ahmedfgad/github-actions
PyGAD 3.5.0
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‎docs/md/releases.md‎

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@@ -606,6 +606,7 @@ Release Date 08 July 2025
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15. More examples are created.
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16. Edited the `sort_solutions_nsga2()` method in the `pygad/utils/nsga2.py` script to accept an optional parameter called `find_best_solution` when calling this method just to find the best solution.
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17. Fixed a bug while applying the non-dominated sorting in the `get_non_dominated_set()` method inside the `pygad/utils/nsga2.py` script. It was swapping the non-dominated and dominated sets. In other words, it used the non-dominated set as if it is the dominated set and vice versa. All the calls to this method were edited accordingly. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/320.
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18. Fix a bug retrieving in the `best_solution()` method when retrieving the best solution for multi-objective problems. https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/331
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# PyGAD Projects at GitHub
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‎docs/source/pygad_more.rst‎

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@@ -530,14 +530,23 @@ assigning a larger value for the ``sample_size`` parameter.
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PyGAD does not yet handle the **dependencies** among the genes in the
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``gene_constraint`` parameter.
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For example, gene 0 might depend on gene 1. To efficiently enforce
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the constraints, the constraint for gene 1 must be enforced first (if
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not ``None``) then the constraint for gene 0.
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This is an example where gene 0 depends on gene 1. To efficiently
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enforce the constraints, the constraint for gene 1 must be enforced
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first (if not ``None``) then the constraint for gene 0.
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.. code:: python
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gene_constraint=
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[
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lambda solution,values: [val for val in values if val<solution[1]],
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lambda solution,values: [val for val in values if val>10]
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]
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PyGAD applies constraints sequentially, starting from the first gene
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to the last. To ensure correct behavior when genes depend on each
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other, structure your GA problem so that if gene X depends on gene Y,
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then gene Y appears earlier in the chromosome (solution) than gene X.
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As a result, its gene constraint will be earlier in the list.
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Full Example
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------------

‎docs/source/releases.rst‎

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8. Refactoring the ``pygad/helper/unique.py`` script to remove
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duplicate codes and reformatting the docstrings.
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9. The plot_pareto_front_curve() method added to the
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9. The ``plot_pareto_front_curve()`` method added to the
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pygad.visualize.plot.Plot class to visualize the Pareto front for
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multi-objective problems. It only supports 2 objectives.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/279
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PyGAD 3.5.0
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-----------
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Release Date 07 July 2025
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Release Date 08 July 2025
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1. Fix a bug when minus sign (-) is used inside the ``stop_criteria``
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parameter for multi-objective problems.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/314
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/323
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2. Fix a bug when the ``stop_criteria`` parameter is passed as an
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iterable (e.g. list) for multi-objective problems (e.g.
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called to ensure the candidate value is valid. Check the `Gene
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Constraint <https://pygad.readthedocs.io/en/latest/pygad_more.html#gene-constraint>`__
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section for more information.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/119
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11. A new parameter called ``sample_size`` is added. To select a gene
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value that respects a constraint, this variable defines the size of
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15. More examples are created.
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16. Edited the ``sort_solutions_nsga2()`` method in the
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``pygad/utils/nsga2.py`` script to accept an optional parameter
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called ``find_best_solution`` when calling this method just to find
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the best solution.
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17. Fixed a bug while applying the non-dominated sorting in the
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``get_non_dominated_set()`` method inside the
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``pygad/utils/nsga2.py`` script. It was swapping the non-dominated
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and dominated sets. In other words, it used the non-dominated set as
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if it is the dominated set and vice versa. All the calls to this
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method were edited accordingly.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/320.
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18. Fix a bug retrieving in the ``best_solution()`` method when
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retrieving the best solution for multi-objective problems.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/331
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PyGAD Projects at GitHub
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========================
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‎example_multi_objective.py‎

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‎pygad/pygad.py‎

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pop_fitness == numpy.max(pop_fitness))[0][0]
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elif pop_fitness_arr.ndim == 2:
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# Multi-objective optimization.
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# Sort by all objectives in descending order.
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# The first objective is the most important, then the second, etc.
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sorted_indices = numpy.lexsort([ -pop_fitness_arr[:,i] for i in reversed(range(pop_fitness_arr.shape[1])) ])
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best_match_idx = sorted_indices[0]
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maximum_fitness_value = pop_fitness_arr[best_match_idx]
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best_match_list = numpy.where(
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pop_fitness == maximum_fitness_value)
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best_match_idx = best_match_list[0][0] # Get the first index of the best match.
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# Use NSGA-2 to sort the solutions using the fitness.
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# Set find_best_solution=True to avoid overriding the pareto_fronts instance attribute.
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best_match_list = self.sort_solutions_nsga2(fitness=pop_fitness,
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find_best_solution=True)
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# Get the first index of the best match.
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best_match_idx = best_match_list[0]
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best_solution = self.population[best_match_idx, :].copy()
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best_solution_fitness = pop_fitness[best_match_idx]

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