| 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://web-olive-one-89.vercel.app/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 objectivesoraclaw-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
- Use
numObjectives: 2+ for Pareto frontier (tradeoff curves between competing goals)
- Tournament selection is best for most problems. Rank-based for wildly varying fitness values.
- Uniform crossover explores more broadly. Single-point is more conservative.
- Set
mutationRate: 0.01-0.05. Adaptive mutation adjusts automatically.
- 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.
