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name: genetic-algorithm description: "Build, tune, run, persist, and troubleshoot pygad.GA for custom single- and multi-objective optimization." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

genetic-algorithm

Use this sub-skill when the task is to configure and operate pygad.GA directly: define a fitness function, shape the population and genes, choose selection/crossover/mutation operators, run single- or multi-objective optimization, inspect the best solution, persist a run, or debug GA validation/runtime errors.

Route elsewhere

• Built-in benchmark problem classes, ZDT/DTLZ/knapsack/TSP, or quality indicators: use the benchmarks sub-skill.
• Plotting, report PDFs, headless matplotlib, or detailed visual summaries: use the results-and-visuals sub-skill.
• NN/GANN/CNN/KerasGA/TorchGA model-weight optimization: use the neural-networks sub-skill.

Operating flow

1. Frame the genome and objective. Decide num_genes, sol_per_pop, value bounds/choices (gene_space), type/precision (gene_type), and whether the fitness is single-objective (return one number) or multi-objective (return list, tuple, or numpy.ndarray).
2. Write a valid fitness function. Public functions must accept (ga_instance, solution, solution_idx). With fitness_batch_size, the second/third arguments become batches and the return must contain one fitness value per solution.
3. Instantiate pygad.GA. Set at least num_generations, num_parents_mating, fitness_func, and either initial_population or both sol_per_pop and num_genes.
4. Tune operators. Use built-ins first (parent_selection_type, crossover_type, mutation_type), then custom callables only when the built-ins cannot express the workflow.
5. Run and inspect. Call ga.run(), then ga.best_solution(ga.last_generation_fitness), ga.run_completed, ga.generations_completed, and ga.best_solution_generation.
6. Persist if needed. Call ga.save(filename_without_pkl) and restore with pygad.load(filename_without_pkl). The .pkl suffix is added automatically.

Reference map

• Constructor signatures, public method contracts, operators, callbacks, attributes, and validation rules: references/api-reference.md
• Recipes for single-objective, multi-objective, adaptive mutation, gene constraints, custom operators, parallel/batch fitness, callbacks, and save/load continuation: references/workflows.md
• Error diagnosis and corrective actions: references/troubleshooting.md

Bundled safe scripts

• scripts/core_ga_smoke.py: deterministic single-objective GA smoke with binary genes, callback assertions, and save/load through a temporary file.
• scripts/multi_objective_template.py: deterministic NSGA-II/NSGA-III template for a two-objective trade-off; accepts --selector for NSGA variants and writes only temporary state.

Safety notes

• PyGAD maximizes fitness. Convert minimization losses to maximization scores, for example with -loss or 1 / (loss + epsilon).
• Keep save_solutions=True and save_best_solutions=True off for large populations unless the user explicitly needs full history.
• Use process-based parallel_processing only when the fitness function is picklable and expensive enough to offset process overhead.
• Do not use external cloud persistence from a GA object unless the user explicitly supplies the service intent, dependency, and credentials.