# genpark-simulated-annealing-metropolis-skill

> genpark-simulated-annealing-metropolis-skill — alphaparkinc-genpark-simulated-annealing-metropolis-skill. Use this tool when you need to optimize complex problems with multiple local minima, as it employs Simulated Annealing with Metropolis criterion to probabilistically accept new solutions and geometric cooling to converge to a global optimum. It takes in a problem definition and initial parameters as input and outputs an optimized solution. Utilize this skill in contexts where traditional optimization methods fail, such as non-convex or non-linear problems.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-simulated-annealing-metropolis-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-simulated-annealing-metropolis-skill

## Description

Simulated Annealing global search with Metropolis probabilistic acceptance criterion and geometric cooling.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-simulated-annealing-metropolis-skill)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alphaparkinc-genpark-simulated-annealing-metropolis-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-simulated-annealing-metropolis-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-simulated-annealing-metropolis-skill/pull`

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