Getting PyGAD 3.5.0 Ready

Author: ahmedfgad

docs/md/releases.md

@@ -606,6 +606,7 @@ Release Date 08 July 2025
 15. More examples are created.
 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.
 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.
+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
 
 # PyGAD Projects at GitHub
 

docs/source/pygad_more.rst

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

docs/source/releases.rst

@@ -1593,7 +1593,7 @@ Release Date 07 January 2025
 8.  Refactoring the ``pygad/helper/unique.py`` script to remove
     duplicate codes and reformatting the docstrings.
 
-9.  The plot_pareto_front_curve() method added to the
+9.  The ``plot_pareto_front_curve()`` method added to the
     pygad.visualize.plot.Plot class to visualize the Pareto front for
     multi-objective problems. It only supports 2 objectives.
     https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/279
@@ -1630,11 +1630,12 @@ Release Date 07 January 2025
 PyGAD 3.5.0
 -----------
 
-Release Date 07 July 2025
+Release Date 08 July 2025
 
 1.  Fix a bug when minus sign (-) is used inside the ``stop_criteria``
     parameter for multi-objective problems.
     https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/314
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/323
 
 2.  Fix a bug when the ``stop_criteria`` parameter is passed as an
     iterable (e.g. list) for multi-objective problems (e.g.
@@ -1726,6 +1727,7 @@ Release Date 07 July 2025
     called to ensure the candidate value is valid. Check the `Gene
     Constraint <https://pygad.readthedocs.io/en/latest/pygad_more.html#gene-constraint>`__
     section for more information.
+    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/119
 
 11. A new parameter called ``sample_size`` is added. To select a gene
     value that respects a constraint, this variable defines the size of
@@ -1755,6 +1757,23 @@ Release Date 07 July 2025
 
 15. More examples are created.
 
+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.
+
+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.
+
+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
+
 PyGAD Projects at GitHub
 ========================
 

example_multi_objective.py

@@ -2237,17 +2237,13 @@ def best_solution(self, pop_fitness=None):
                  pop_fitness == numpy.max(pop_fitness))[0][0]
             elif pop_fitness_arr.ndim == 2:
                 # Multi-objective optimization.
-                # Sort by all objectives in descending order.
-                # The first objective is the most important, then the second, etc.
-                sorted_indices = numpy.lexsort([ -pop_fitness_arr[:,i] for i in reversed(range(pop_fitness_arr.shape[1])) ])
-                best_match_idx = sorted_indices[0]
-                maximum_fitness_value = pop_fitness_arr[best_match_idx]
-
-                best_match_list = numpy.where(
-                    pop_fitness == maximum_fitness_value)
-            
-                best_match_idx = best_match_list[0][0]  # Get the first index of the best match.
+                # Use NSGA-2 to sort the solutions using the fitness.
+                # Set find_best_solution=True to avoid overriding the pareto_fronts instance attribute.
+                best_match_list = self.sort_solutions_nsga2(fitness=pop_fitness,
+                                                            find_best_solution=True)
 
+                # Get the first index of the best match.
+                best_match_idx = best_match_list[0]
 
             best_solution = self.population[best_match_idx, :].copy()
             best_solution_fitness = pop_fitness[best_match_idx]