8000 Solve a bug in NSGA2 tournament selection of parents · ahmedfgad/GeneticAlgorithmPython@3c7ed23 · GitHub
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Ahmed Gad
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Solve a bug in NSGA2 tournament selection of parents
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‎docs/md/pygad_more.md

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> PyGAD does not yet handle the **dependencies** among the genes in the `gene_constraint` parameter.
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>
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> 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.
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> 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.
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>
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> 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.
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> ```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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> ```
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>
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> 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.
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## Full Example
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‎docs/md/releases.md

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6. Created a new method called `unique_float_gene_from_range()` inside the `pygad.helper.unique.Unique` class to find a unique floating-point number from a range.
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7. Fix a bug in the `pygad.helper.unique.Unique.unique_gene_by_space()` method to return the numeric value only instead of a NumPy array.
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8. Refactoring the `pygad/helper/unique.py` script to remove duplicate codes and reformatting the docstrings.
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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
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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
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11. Fix a bug converting a nested NumPy array to a nested list. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/300
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12. The `Matplotlib` library is only imported when a method inside the `pygad/visualize/plot.py` script is used. This is more efficient than using `import matplotlib.pyplot` at the module level as this causes it to be imported when `pygad` is imported even when it is not needed. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/292
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13. Fix a bug when minus sign (-) is used inside the `stop_criteria` parameter (e.g. `stop_criteria=["saturate_10", "reach_-0.5"]`). https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/296
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## PyGAD 3.5.0
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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` parameter for multi-objective problems. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/314
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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
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2. Fix a bug when the `stop_criteria` parameter is passed as an iterable (e.g. list) for multi-objective problems (e.g. `['reach_50_60', 'reach_20, 40']`). https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/314
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3. Call the `get_matplotlib()` function from the `plot_genes()` method inside the `pygad.visualize.plot.Plot` class to import the matplotlib library. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/315
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4. Create a new helper method called `select_unique_value()` inside the `pygad/helper/unique.py` script to select a unique gene from an array of values.
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11. `filter_gene_values_by_constraint()`: Receives a list of values for a gene. Then it filters such values using the gene constraint.
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12. `get_valid_gene_constraint_values()`: Selects one valid gene value that satisfy the gene constraint. It simply calls `generate_gene_value()` to generate some gene values then it filters such values using `filter_gene_values_by_constraint()`.
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9. Create a new helper method called `mutation_process_random_value()` inside the `pygad/utils/mutation.py` script that generates constrained random values for mutation. It calls either `generate_gene_value()` or `get_valid_gene_constraint_values()` based on whether the `gene_constraint` parameter is used or not.
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10. A new parameter called `gene_constraint` is added. It accepts a list of callables (i.e. functions) acting as constraints for the gene values. Before selecting a value for a gene, the callable is 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.
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10. A new parameter called `gene_constraint` is added. It accepts a list of callables (i.e. functions) acting as constraints for the gene values. Before selecting a value for a gene, the callable is 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
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11. A new parameter called `sample_size` is added. To select a gene value that respects a constraint, this variable defines the size of the sample from which a value is selected randomly. Useful if either `allow_duplicate_genes` or `gene_constraint` is used. An instance attribute of the same name is created in the instances of the `pygad.GA` class. Check the [sample_size Parameter](https://pygad.readthedocs.io/en/latest/pygad_more.html#sample-size-parameter) section for more information.
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12. Use the `sample_size` parameter instead of `num_trials` in the methods `solve_duplicate_genes_randomly()` and `unique_float_gene_from_range()` inside the `pygad/helper/unique.py` script. It is the maximum number of values to generate as the search space when looking for a unique float value out of a range.
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13. Fixed a bug in population initialization when `allow_duplicate_genes=False`. Previously, gene values were checked for duplicates before rounding, which could allow near-duplicates like 7.61 and 7.62 to pass. After rounding (e.g., both becoming 7.6), this resulted in unintended duplicates. The fix ensures gene values are now rounded before duplicate checks, preventing such cases.
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14. More tests are created.
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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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# PyGAD Projects at GitHub
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‎docs/md/utils.md

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The `pygad.utils.nsga2` module has a class named `NSGA2` that implements NSGA-II. The methods inside this class are:
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1. `non_dominated_sorting()`: Returns all the pareto fronts by applying non-dominated sorting over the solutions.
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2. `get_non_dominated_set()`: Returns the set of non-dominated solutions from the passed solutions.
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2. `get_non_dominated_set()`: Returns the 2 sets of non-dominated solutions and dominated solutions from the passed solutions. Note that the Pareto front consists of the solutions in the non-dominated set.
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3. `crowding_distance()`: Calculates the crowding distance for all solutions in the current pareto front.
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4. `sort_solutions_nsga2()`: Sort the solutions. If the problem is single-objective, then the solutions are sorted by sorting the fitness values of the population. If it is multi-objective, then non-dominated sorting and crowding distance are applied to sort the solutions.
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‎example_multi_objective.py

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import pygad
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import numpy
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"""
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Given these 2 functions:
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y1 = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6
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y2 = f(w1:w6) = w1x7 + w2x8 + w3x9 + w4x10 + w5x11 + 6wx12
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where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=50
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and (x7,x8,x9,x10,x11,x12)=(-2,0.7,-9,1.4,3,5) and y=30
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What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize these 2 functions.
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This is a multi-objective optimization problem.
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PyGAD considers the problem as multi-objective if the fitness function returns:
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1) List.
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2) Or tuple.
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3) Or numpy.ndarray.
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"""
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function_inputs1 = [4,-2,3.5,5,-11,-4.7] # Function 1 inputs.
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function_inputs2 = [-2,0.7,-9,1.4,3,5] # Function 2 inputs.
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desired_output1 = 50 # Function 1 output.
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desired_output2 = 30 # Function 2 output.
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def fitness_func(ga_instance, solution, solution_idx):
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output1 = numpy.sum(solution*function_inputs1)
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output2 = numpy.sum(solution*function_inputs2)
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fitness1 = 1.0 / (numpy.abs(output1 - desired_output1) + 0.000001)
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fitness2 = 1.0 / (numpy.abs(output2 - desired_output2) + 0.000001)
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return [fitness1, fitness2]
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num_generations = 1 # Number of generations.
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num_parents_mating = 5 # Number of solutions to be selected as parents in the mating pool.
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sol_per_pop = 10 # Number of solutions in the population.
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num_genes = len(function_inputs1)
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ga_instance = pygad.GA(num_generations=num_generations,
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num_parents_mating=num_parents_mating,
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sol_per_pop=sol_per_pop,
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num_genes=num_genes,
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fitness_func=fitness_func,
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random_seed=3,
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parent_selection_type='tournament_nsga2')
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# Running the GA to optimize the parameters of the function.
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ga_instance.run()
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"""
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ga_instance.plot_fitness(label=['Obj 1', 'Obj 2'])
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ga_instance.plot_pareto_front_curve()
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# Returning the details of the best solution.
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solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
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print(f"Parameters of the best solution : {solution}")
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print(f"Fitness value of the best solution = {solution_fitness}")
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print(f"Index of the best solution : {solution_idx}")
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prediction = numpy.sum(numpy.array(function_inputs1)*solution)
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print(f"Predicted output 1 based on the best solution : {prediction}")
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prediction = numpy.sum(numpy.array(function_inputs2)*solution)
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print(f"Predicted output 2 based on the best solution : {prediction}")
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if ga_instance.best_solution_generation != -1:
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print(f"Best fitness value reached after {ga_instance.best_solution_generation} generations.")
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"""

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