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name: default_strategy_parameters description: Compute CMA-ES default strategy parameters from the tutorial formulas for bounded recovery experiments.

Default CMA-ES Strategy Parameters

Use this skill when a recovery or implementation needs dimension-aware CMA-ES defaults rather than hand-picked optimizer constants. Do not use it for gradient optimizers or algorithms that do not adapt a Gaussian covariance matrix.

Inputs

• Search dimension n, a positive integer.
• Optional population size lambda_; if absent use 4 + floor(3 ln n).

Outputs

• lambda, mu, positive recombination weights, mueff, cm, cs, ds, cc, c1, cmu, and expected_norm.

Workflow

1. Validate the dimension and population size.
2. Compute log-shaped positive parent weights and normalize them to sum to one.
3. Compute effective parent mass and learning rates using the tutorial's Appendix A defaults.
4. Return JSON-serializable values so downstream skills can reproduce the run.

Validation

Run python scripts/cmaes_parameters.py --dimension 2 and python -m pytest tests or the Distiller skill-tree validator with --run-tests.

Limitations

This skill implements the positive-weight default sufficient for the reduced recovery; active negative covariance weights are documented but not required by the proxy experiment.