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🦀 ClawHub

Oraclaw Evolve

by @whatsonyourmind

Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem w...

Versionv1.0.0
Downloads476
#finance#legal#automation
TERMINAL
clawhub install oraclaw-evolve

📖 About This Skill

name: oraclaw-evolve description: Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem with multiple competing goals. Handles nonlinear search spaces that LP solvers cannot. version: 1.0.0 metadata: openclaw: requires: env: - ORACLAW_API_KEY primaryEnv: ORACLAW_API_KEY emoji: "🧬" homepage: https://oraclaw.dev/evolve tags: - genetic-algorithm - optimization - pareto - multi-objective - portfolio - hyperparameter - evolutionary price: 0.15 currency: USDC

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.

    🔒 Constraints

    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.