Oraclaw oraclaw-evolve

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.

install

source · Clone the upstream repo

git clone https://github.com/Whatsonyourmind/oraclaw

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/Whatsonyourmind/oraclaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/mission-control/packages/clawhub-skills/oraclaw-evolve" ~/.claude/skills/whatsonyourmind-oraclaw-oraclaw-evolve && rm -rf "$T"

manifest: mission-control/packages/clawhub-skills/oraclaw-evolve/SKILL.md

source content

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

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.