oraclaw-cmaes

# 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` ```json { "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.