---

name: scikit-opt description: "Use scikit-opt to solve GA, DE, PSO, SA, AFSA, routing, and objective-speedup workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

scikit-opt

Use this repo skill for the scikit-opt / sko Python package when the task is about heuristic optimization, route/permutation search, objective-function shaping, or run-mode acceleration.

Install and import

• Install the published package with pip install scikit-opt.
• Check the import with python -c "import sko; print(sko.__version__)".
• If you are working from a local checkout during maintenance, see references/troubleshooting.md for the editable-install caveat; the runtime skill itself stays package-centric.

Read this first when unsure

• Read references/package-overview.md for the family map: GA, DE, PSO, SA, AFSA, TSP/routing, built-in demo functions, and run-mode helpers.
• Read references/troubleshooting.md for install/import issues, optional dependencies, the PSO_TSP caveat, and common runtime failures.
• Read references/repo-provenance.md when you need to know whether this skill still matches the current repository snapshot.
• Run scripts/check_scikit_opt_env.py when you want a quick environment sanity check without opening the source repository.

Route to the right sub-skill

• genetic-algorithms — GA, EGA, RCGA, integer or mixed-precision search, continuation runs, and custom operators via register().
• continuous-optimizers — DE, PSO, SA schedules, and AFSA for continuous or constrained real-valued problems.
• routing-and-combinatorial — GA_TSP, SA_TSP, ACA_TSP, IA_TSP, fixed-endpoint route recipes, and permutation validation.
• objective-functions-and-speedups — objective shape contracts, built-in benchmark functions, set_run_mode, cached/vectorized/threaded modes, and optional GPU/plotting dependencies.

Minimal routing rules

1. If the task is about GA-style discrete or mixed-precision optimization, go to genetic-algorithms.
2. If the task is about a continuous objective, pick continuous-optimizers.
3. If the task is about permutations, routes, or TSP, pick routing-and-combinatorial.
4. If the task is about objective shape, demo functions, run modes, caching, or optional acceleration, pick objective-functions-and-speedups.

Package facts to remember

• The import package is sko.
• The package version captured in this skill snapshot is 0.6.6.
• Core package dependencies are NumPy and SciPy.
• Plotting, Pandas, Joblib, and PyTorch are optional surfaces that belong to specific workflows rather than the core package import.
• PSO_TSP is a version-specific caveat in this snapshot and should not be treated as a reliable default route solver.

When to use the shared helpers

• Use scripts/check_scikit_opt_env.py before a handoff when you want an import/signature sanity check.
• Read references/package-overview.md before choosing a sub-skill if the user only names a problem shape, not a class.
• Read references/troubleshooting.md before investigating a failure so you can separate install/import problems from algorithm-specific issues.