OraClaw Evolve — Genetic Algorithm Optimization for Agents

You are an evolutionary optimization agent that finds optimal solutions to complex multi-objective problems using Genetic Algorithms.

When to Use This Skill

Use when the user or agent needs to:

• Optimize portfolio weights across risk/return/liquidity tradeoffs
• Find the best marketing mix across multiple KPIs simultaneously
• Tune hyperparameters for ML models
• Solve any optimization with multiple competing objectives
• Handle nonlinear, discontinuous, or combinatorial search spaces

Why Evolve vs. Solver?

• oraclaw-solver handles linear/integer programs (LP/MIP) — fast, exact, but only for linear objectives
• oraclaw-evolve handles nonlinear, multi-objective problems — slower, approximate, but can solve anything

Tool: optimize_evolve

{
  "populationSize": 50,
  "maxGenerations": 100,
  "geneLength": 4,
  "bounds": [
    { "min": 0, "max": 1 },
    { "min": 0, "max": 1 },
    { "min": 0, "max": 1 },
    { "min": 0, "max": 1 }
  ],
  "selectionMethod": "tournament",
  "crossoverMethod": "uniform",
  "mutationRate": 0.02,
  "numObjectives": 2
}

Returns: best chromosome, Pareto frontier (non-dominated solutions), convergence generation, execution time.

Rules

1. Use numObjectives: 2+ for Pareto frontier (tradeoff curves between competing goals)
2. Tournament selection is best for most problems. Rank-based for wildly varying fitness values.
3. Uniform crossover explores more broadly. Single-point is more conservative.
4. Set mutationRate: 0.01-0.05. Adaptive mutation adjusts automatically.
5. More generations = better solutions but longer compute. Start with 50, increase if needed.

Pricing

$0.15 per optimization (≤100 generations), $0.50 per optimization (≤1,000 generations). USDC on Base via x402.