genetic-algorithm-optimizer
Genetic algorithm skill for complex optimization problems with non-linear objectives or discontinuous search spaces
npx skills add a5c-ai/babysitter --skill genetic-algorithm-optimizer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Genetic Algorithm Optimizer ## Overview The Genetic Algorithm Optimizer skill provides evolutionary computation capabilities for solving complex optimization problems that are difficult for traditional methods. It handles non-linear, non-convex, discontinuous, and multi-objective optimization through biologically-inspired search strategies. ## Capabilities - Chromosome encoding (binary, real, permutation) - Selection operators (tournament, roulette, rank) - Crossover and mutation operations - Multi-objective optimization (NSGA-II, NSGA-III) - Constraint handling - Parameter tuning guidance - Convergence monitoring - Pareto front visualization ## Used By Processes - Prescriptive Analytics and Optimization - Strategic Portfolio Optimization - Design Optimization ## Usage ### Problem Definition ```python # Define optimization problem ga_problem = { "name": "Portfolio Optimization", "encoding": "real", # or "binary", "permutation", "integer" "variables": { "asset_weights": { "count": 10, "bounds": [0, 1], "constraint": "sum_to_one" } }, "objectives": [ { "name": "maximize_return", "function": "portfolio_return(weights, expected_returns)", "direction": "maximize" }, { "name": "minimiz
- Overview
- Capabilities
- Used By Processes
- Usage
- Problem Definition
- GA Configuration
- Multi-Objective Configuration (NSGA-II)
- Encoding Types
- Selection Methods
- Input Schema
- Output Schema
- Best Practices
- Constraint Handling
- Multi-Objective Interpretation
What does the genetic-algorithm-optimizer skill do?
Genetic algorithm skill for complex optimization problems with non-linear objectives or discontinuous search spaces
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill genetic-algorithm-optimizer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.