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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
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.
PyGAD 3.3.1
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Merge pull request #277 from ahmedfgad/master
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Improvement on memory usage
In the crossover function, the split point is chosen in the range of the offspring size, instead of the solution size. An out-of-range split point is silently ignored by the range selection: it selects all genes from parent1, and none from parent2. However, the error can be demonstrated by throwing an exception if the split point is out of range.
Fix a typo in example_custom_operators
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1. Validation logic is applied to validate the `num_generations` parameter. 2. The `num_generations` parameter must be assigned a positive integer. Previously, any number (positive/negative, int/float) was accepted. 3. A new script called `activation.py` is added into the `pygad.helper` module to include the activation function used by the `cnn` and `nn` modules. 4. In the `pygad.parent_selection.ParentSelection` class, the `stochastic_universal_selection()` method now calls the `wheel_cumulative_probs()` method instead of repeating the code of calculating the probabilities used for parent selection. 5. The `wheel_cumulative_probs()` method in the `pygad.parent_selection.ParentSelection` class is refactored to reduce its computational time. 6. Use `numpy.where()` to decide which the source parent of each gene within the `uniform_crossover()` method in the `utils/crossover.py` script. The same was already applied to the `scattered_crossover()` method. 7. Add tests for the following modules: 1. `nn` 2. `cnn` 3. `gacnn` 4. `kerasga` 5. `torchga` 8. Fix a bug in the `visualize/plot.py` script where the `labels` parameter of `boxplot()` has been renamed `tick_labels` in Matplotlib. 9. Fix a bug where the `best_solutions_fitness` list (instance attribute to `pygad.GA`) has the fitness of the last generation duplicated when an early stop happens inside the `on_generation()` callback. This made its size incompatible with the `best_solutions` list. 10. The documentation is refactored to solve many language issues and the Furo theme is applied. For easy navigation, the index is reformatted to only show the main sections. At each page, its index is shown at the right side. A new theme toggle button to change theme between light and dark. 11. Support of multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm III (NSGA-III). NSGA-III replaces the crowding distance of NSGA-II with niching against a structured grid of reference points, so it scales better to problems with 4 or more objectives. The new `NSGA3` class lives in the new `pygad/utils/nsga3.py` script and is mixed into the `pygad.GA` class the same way `NSGA2` is. 12. Two new parent selection methods are added to support NSGA-III: 1) `nsga3_selection()` for plain NSGA-III selection, and 2) `tournament_selection_nsga3()` for the tournament variant. Use them by setting `parent_selection_type` to `'nsga3'` or `'tournament_nsga3'`. 13. A new parameter `nsga3_num_divisions` is added to the `pygad.GA` constructor. It is required when `parent_selection_type` is `'nsga3'` or `'tournament_nsga3'` and sets the number of divisions per objective axis used to build the structured reference points (the `p` parameter from Deb & Jain 2014). The total number of reference points is `C(M + p - 1, p)` where `M` is the number of objectives. 14. When `sol_per_pop` is smaller than the number of NSGA-III reference points, PyGAD raises a warning and grows the population to match before the generational loop starts. 15. A new crossover operator: Simulated Binary Crossover (SBX). Use it by setting `crossover_type='sbx'`. The shape of the spread is controlled by the new `sbx_crossover_eta` parameter (default 30). 16. A new mutation operator: polynomial mutation. Use it by setting `mutation_type='polynomial'`. The size of the change is controlled by the new `polynomial_mutation_eta` parameter (default 20). 17. Two new stop criteria: `time_<seconds>` stops the run when the time inside `run()` is at least the given number of seconds; `evaluations_<N>` stops the run when the number of fitness function calls reaches the given count. New instance attribute `num_fitness_evaluations` counts the calls. 18. A new submodule `pygad.utils.quality_indicators` with four functions to measure the quality of a Pareto front: `hypervolume`, `inverted_generational_distance`, `generational_distance`, and `spacing`. 19. A new submodule `pygad.benchmarks` with built-in benchmark problems. `pygad.benchmarks.classic` has Sphere, Rastrigin, Rosenbrock, Griewank, Schwefel, Ackley, and Himmelblau. `pygad.benchmarks.zdt` has the ZDT family (ZDT1, ZDT2, ZDT3, ZDT4, ZDT6). `pygad.benchmarks.dtlz` has DTLZ1, DTLZ2, DTLZ3, and DTLZ4. `pygad.benchmarks.knapsack` has the 0/1 Knapsack problem. Each class is callable with the PyGAD fitness signature and returns negated values (for the minimization-style problems) so PyGAD can maximize toward the original minimum. 20. Update the documentation to reflect the recent additions and changes to the library structure. 21. A new benchmark `pygad.benchmarks.tsp` with a `TSP` class for the Travelling Salesman Problem. The class accepts either 2D `coordinates` or a precomputed `distance_matrix`, exposes `gene_space`, `gene_type`, and `allow_duplicate_genes` for the permutation encoding, and returns the negative tour length as the fitness. 22. Two new example folders under `/examples`: `examples/benchmarks/` has one runnable example per benchmark (classic, ZDT, DTLZ, knapsack, and TSP), and `examples/quality_indicators/` has one runnable example per quality indicator (hypervolume, IGD, GD, and spacing). 23. `plot_pareto_front_curve()` now also supports 3 objectives (3D scatter). M >= 4 still raises and points to the new high-dimensional plots. 24. Seven new plot methods on `pygad.GA`. The first three work on the final population (no extra flag needed): `plot_pareto_front_pcp()` (parallel coordinates, any M >= 2), `plot_pareto_front_scatter_matrix()` (M-by-M pairwise scatter, best for M >= 4), and `plot_pareto_front_heatmap()` (solutions-by-objectives heatmap). The other four require `save_solutions=True`: `plot_fitness_band()` (per-generation min / mean / max with a shaded band), `plot_non_dominated_hypervolume()` (hypervolume of the non-dominated set per generation), `plot_population_diversity()` (mean pairwise distance per generation), and `plot_pareto_front_evolution()` (non-dominated set overlaid every k generations). 25. Fix a latent divide-by-zero in `NSGA3.nsga3_normalize_fitness()`. The safeguard for near-zero denominators used to collapse to `0` for tiny negative values (the realistic case under PyGAD-max), which silently produced wrong normalized values. The safeguard now keeps the negative sign. 26. Refactor the NSGA classes to keep each script focused. A new module `pygad/utils/nsga.py` hosts the `NSGA` mixin with `non_dominated_sorting()` and `get_non_dominated_set()`, which are shared between NSGA-II and NSGA-III. `nsga2.py` now only carries NSGA-II specific code (`crowding_distance`, `sort_solutions_nsga2`). `nsga3.py` now only carries the NSGA-III algorithm primitives. The `nsga3_selection()` and `tournament_selection_nsga3()` methods have moved to `pygad/utils/parent_selection.py` next to their NSGA-II counterparts. The engine-time helpers `_bootstrap_nsga3_reference_points()`, `_nsga3_grow_population()`, `_nsga3_generate_extra_random_solutions()`, and `_nsga3_generate_single_random_gene()` now live in `pygad/utils/engine.py`. 27. Rename NSGA-III novel names to start with `nsga3_` so the algorithm-specific surface is easy to spot. Algorithm primitives become `nsga3_generate_reference_points`, `nsga3_compute_ideal_point`, `nsga3_find_extreme_points`, `nsga3_compute_intercepts`, `nsga3_normalize_fitness`, `nsga3_associate_to_reference_points`, and `nsga3_niching_select`. Module-level helpers gain the same prefix (`_nsga3_pick_target_reference_point`, `_nsga3_pick_candidate_at_reference`, `_nsga3_enumerate_compositions`, `_nsga3_validate_multi_objective_fitness`, `_nsga3_accumulate_fronts`). The constants are renamed `NSGA3_ASF_EPSILON` and `NSGA3_INTERCEPT_NEAR_ZERO`. Names that already had NSGA-II parallels (`tournament_selection_nsga3`, `pareto_fronts`, `non_dominated_sorting`) keep their original spelling. 28. Spell every name and docstring in American English (`normalize`, `maximize`, `behavior`, `color`, `optimization`, ...) so the library stays consistent. 29. Expand abbreviated names introduced by the NSGA-III refactor: `fl_indices` to `critical_front_indices`, `fl_assoc` to `critical_front_associations`, `fl_dist` to `critical_front_distances`, `st_indices` to `selection_pool_indices`, `st_fitness` to `selection_pool_fitness`, `accepted_assoc` to `accepted_associations`, `K` to `num_to_select` (in `nsga3_niching_select`). 30. The NSGA-III population auto-growth path now respects every initial-population rule: `init_range_low`/`init_range_high`, `gene_space`, `gene_type` (single dtype or nested per-gene `[type, precision]`), `gene_constraint`, and `allow_duplicate_genes=False`. Previously, only the gene-space / init-range sampling step was applied; gene constraints and duplicate resolution were skipped, which could leave the grown rows in an invalid state. 31. A new `Report` mixin in `pygad/utils/report.py` adds `ga_instance.generate_report(filename, ...)` to build a PDF report of the run. The report bundles a configuration table, a run-summary table, the best solution, and every applicable plot (auto-selected based on the run's properties: SOO vs MOO, number of objectives, `save_solutions`, `save_best_solutions`). The report uses `reportlab` and `matplotlib`, both available through the new optional dependency extra `pip install pygad[report]`. 32. A new example `examples/example_generate_report.py` shows how to build a PDF report after running a multi-objective GA. 33. The `pygad.md`, `releases.md`, `visualize.md`, and `utils.md` documentation pages were updated to reflect the new module layout, the renamed methods, the new `generate_report()` entry point, and the new NSGA-III instance attributes (`nsga3_num_divisions`, `nsga3_reference_points`). The "Other Instance Attributes & Methods" section in `pygad.md` is now grouped by area (Lifecycle, Population, Fitness, Parent Selection, NSGA-II, NSGA-III, Crossover, Mutation, Elitism, Gene Constraints, Saving) so each method or attribute appears under its topic. 34. Fix issue #351 by updating the documentation to clarify what the `solution` has. 35. Version changed in the following modules: 1. A new submodule `pygad.benchmarks` is added with the version `1.0.0`. 2. The version of the `pygad.utils` submodule is upgraded from `1.4.0` to `1.5.0`. 3. The version of the `pygad.helper` submodule is upgraded from `1.3.0` to `1.4.0`. 4. The version of the `pygad.visualize` submodule is upgraded from `1.1.1` to `1.2.0`. 5. The version of the `pygad.nn` submodule is upgraded from `1.2.2` to `1.2.3`. 6. The version of the `pygad.cnn` submodule is upgraded from `1.1.1` to `1.1.2`. 7. The version of the `pygad.kerasga` submodule is upgraded from `1.3.1` to `1.3.2`. 8. The version of the `pygad.torchga` submodule is upgraded from `1.4.1` to `1.4.2`. 9. The version of the `pygad.gann` submodule is upgraded from `1.0.0` to `1.0.1`. 10. The version of the `pygad.gacnn` submodule is upgraded from `1.0.0` to `1.0.1`. 36. The PDF report built by `generate_report()` now shows the PyGAD logo on its title page. The logo image ships with the package, so no network access is needed. If the image file is missing, the report is built without it. 37. Two private helper functions are added to the `pygad/utils/report.py` script for the logo. `_pdf_report_read_logo_bytes()` reads the bundled logo file and returns its bytes, or `None` if the file is missing. `_pdf_report_build_logo_image()` builds the image that is placed on the title page, or returns `None` so the report still builds without the logo. 38. The private helper functions in the `pygad/utils/report.py` script are renamed to start with the `_pdf_report_` prefix so their purpose is clear from the name. For example, `_build_title_section()` becomes `_pdf_report_build_title_section()` and `_render_plot_to_png()` becomes `_pdf_report_render_plot_to_png()`.
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predict() cloned the whole model on every call and then used model.predict(). When predict() runs once per solution inside a fitness function, this is slow. It now sets the solution as the model weights, restores the original weights at the end, and calls the model directly when no batching is asked for. The output is the same and it runs much faster. model.predict() is still used when batch_size or steps is given.
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Refer to tutorials to use vilvik
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