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name: benchmarks description: "Use PyGAD benchmark problem classes and quality indicators for classic, ZDT/DTLZ, knapsack, and TSP optimization workflows." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

benchmarks

Use this sub-skill when the task is to work with the built-in benchmark problem families under pygad.benchmarks or the quality indicators in pygad.utils.quality_indicators.

Route elsewhere

• Custom GA tuning, callbacks, persistence, or general optimization debugging: use the genetic-algorithm sub-skill.
• Plotting benchmark fronts, report generation, or headless figure handling: use the results-and-visuals sub-skill.

What this sub-skill covers

• Classic continuous problems: Sphere, Rastrigin, Rosenbrock, Griewank, Schwefel, Ackley, and Himmelblau.
• Multi-objective test suites: ZDT1, ZDT2, ZDT3, ZDT4, ZDT6, DTLZ1, DTLZ2, DTLZ3, and DTLZ4.
• Combinatorial problems: 0/1 Knapsack and TSP.
• Quality indicators: hypervolume, inverted generational distance, generational distance, and spacing.

Operating flow

1. Pick the benchmark family. Match the problem to the target shape:
   • classic single-objective minima,
   • ZDT trade-off fronts,
   • DTLZ many-objective fronts,
   • knapsack selection, or
   • TSP permutations.
2. Match the genome to the benchmark attributes.
   • Continuous benchmark classes expose num_genes, num_objectives, and bounds.
   • Knapsack exposes gene_space=[0, 1] and gene_type=int.
   • TSP exposes gene_space=list(range(num_cities)), gene_type=int, and allow_duplicate_genes=False.
3. Remember PyGAD maximizes. The benchmark callables already return values in maximization form, so do not negate them again.
4. Choose a compatible GA setup.
   • Continuous benchmarks usually pair with init_range_low/high from the benchmark bounds and real-coded operators such as SBX plus polynomial mutation.
   • ZDT and DTLZ runs should use NSGA-style parent selection.
   • Knapsack and TSP should use integer/permutation-aware gene settings.
5. Evaluate quality indicators on the final front. Use the final ga.last_generation_fitness matrix and, for ZDT, the bundled pareto_front() helper when available.
6. Use the smoke script for a quick end-to-end check. It runs compact Sphere, ZDT1, DTLZ2, Knapsack, and TSP examples and saves state only in temporary files.

Reference map

• Public API signatures, default values, return contracts, and validation rules: references/api-reference.md
• Recommended benchmark workflows and indicator recipes: references/workflows.md
• Common failures and fixes: references/troubleshooting.md
• Deterministic bundled smoke script: scripts/benchmark_smoke.py

Validation signals

• Single-objective benchmark runs return a scalar fitness and ga.best_solution(...) should approach the known optimum.
• ZDT and DTLZ runs return vector fitness with ga.last_generation_fitness.shape[1] == problem.num_objectives.
• problem.pareto_front() is available for ZDT1, ZDT2, ZDT4, and ZDT6 and returns negated reference-front points in PyGAD maximization format.
• hypervolume requires a reference point that is smaller than every fitness vector entry.
• spacing returns 0.0 for fewer than two solutions.