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name: pygad description: "Route PyGAD genetic-algorithm optimization, benchmarks, visualization/reporting, and neural-model weight workflows." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

PyGAD repo skill

Use this repo skill when a task involves PyGAD, the Python genetic-algorithm package from GeneticAlgorithmPython: creating or tuning pygad.GA runs, solving single- or multi-objective optimization problems, using bundled benchmark problems, plotting/reporting completed runs, or optimizing neural-network weights through PyGAD helpers.

First checks

Install the core package when PyGAD is not already available:

python -m pip install pygad

Then verify the import in the same environment that will run the optimization:

import pygad
print(pygad.__version__)

Core PyGAD usage needs numpy and cloudpickle. Optional plotting/reporting and deep-learning integrations need extra dependencies; see references/installation.md before installing heavy extras.

Route by task

If the user asks for...	Read this sub-skill
Build or debug a pygad.GA fitness function, population, genes, operators, callbacks, stop criteria, save/load, single-objective optimization, or NSGA-II/NSGA-III custom multi-objective run	sub-skills/genetic-algorithm/SKILL.md
Use built-in benchmark classes (Sphere, ZDT, DTLZ, Knapsack, TSP) or quality indicators (hypervolume, IGD, GD, spacing)	sub-skills/benchmarks/SKILL.md
Summarize, plot, export PNGs, handle headless matplotlib, configure logging, or generate a PDF report from a completed GA run	sub-skills/results-and-visuals/SKILL.md
Optimize dense NN/CNN/Keras/PyTorch model weights with PyGAD (pygad.nn, gann, cnn, gacnn, kerasga, torchga)	sub-skills/neural-networks/SKILL.md

Root references and scripts

• references/capability-map.md: high-level package map, module boundaries, and cross-sub-skill dependencies.
• references/installation.md: core install, optional extras, import checks, and backend/dependency choices.
• references/troubleshooting.md: cross-cutting install/import, optional dependency, runtime, and API triage.
• references/repo-provenance.md: source commit, version, and evidence baseline for refresh decisions.
• scripts/pygad_quick_check.py: safe import/API smoke that runs a tiny GA and reports optional dependency availability.
• scripts/run_skill_smokes.py: optional local verification helper that runs the bundled deterministic smoke scripts in this skill tree.

Operating priorities

1. Keep PyGAD's maximization convention explicit. Convert losses/minimization objectives to higher-is-better fitness before constructing pygad.GA.
2. Validate shapes early. Most failures are inconsistent initial_population, num_genes, gene_space, custom operators, batch fitness returns, or model-weight vector restoration.
3. Start with tiny deterministic runs. Use random_seed, small populations, and short generation counts before scaling to expensive objective functions.
4. Install only needed extras. Plot/report dependencies and deep-learning frameworks are optional. Do not install TensorFlow/PyTorch unless the user's workflow requires them.
5. Use bundled references/scripts instead of the original checkout. This skill is self-contained; runtime guidance should not depend on reopening repository examples or docs.

Handoff notes

• If the user is asking to perform a new downstream optimization experiment, load the relevant sub-skill in Researcher mode and execute the experiment there.
• If source APIs or docs changed after the provenance commit, refresh this repo skill before relying on old defaults.
• If a task depends on external services such as Vilvik cloud export, confirm credentials, service intent, and dependency installation separately; the safe default is local PyGAD execution only.