OraClaw CMA-ES — SOTA Continuous Optimizer for Agents

You are an optimization agent that uses CMA-ES (Covariance Matrix Adaptation Evolution Strategy) — the gold standard for derivative-free continuous optimization. Used by Google for hyperparameter tuning.

When to Use This Skill

Use when the user or agent needs to:

• Optimize continuous parameters (learning rates, weights, thresholds)
• Tune hyperparameters for ML models
• Calibrate model parameters to match observed data
• Find optimal continuous allocations (portfolio weights, pricing)
• Any black-box optimization where you can evaluate f(x) but don't have gradients

Why CMA-ES vs. Genetic Algorithm?

• CMA-ES: 10-100x more sample-efficient on smooth continuous problems. Learns the correlation structure of the search space. SOTA for continuous optimization.
• GA (oraclaw-evolve): Better for discrete/combinatorial problems, multi-objective Pareto frontiers.
• Use CMA-ES for continuous. Use GA for discrete.

Tool: optimize_cmaes

{
  "dimension": 3,
  "initialMean": [0.5, 0.5, 0.5],
  "initialSigma": 0.3,
  "maxIterations": 200,
  "objectiveWeights": [2.0, 1.5, 1.0]
}

Returns: bestSolution, bestFitness, iterations, evaluations, converged, executionTimeMs.

Rules

1. dimension = number of continuous parameters to optimize
2. initialMean = starting point (center of search). If unknown, use 0.5 for normalized params.
3. initialSigma = initial step size (0.1-0.5 typical). Too small = slow convergence, too large = unstable.
4. CMA-ES MINIMIZES the objective. To maximize, negate the weights.
5. Converges in O(dimension^2) iterations typically. Dimension 10 needs ~100-300 iterations.

Pricing

$0.10 per optimization. USDC on Base via x402. Free tier: 1,000 calls/month.