28 lines
1.3 KiB
Markdown
28 lines
1.3 KiB
Markdown
# Genetic Algorithms
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## Definition
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A class of ML algorithms inspired by biological evolution that uses selection, crossover, and mutation to iteratively improve solutions. The Knock-out multi-agent pattern is a lean implementation of genetic algorithms applied to LLM agents.
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## Core Components
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1. **Genetic Representation** — A model and its context represent a solution candidate
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2. **Fitness Function** — Evaluates how well each candidate solves the problem
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3. **Selection** — Winners are chosen based on fitness scores
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4. **Crossover** — Combining traits from successful candidates (optional in basic knock-out)
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5. **Mutation** — Random variation in candidate traits (optional)
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## Application to Multi-Agent Knock-out
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- N agents work on the same task
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- Validator (fitness function) evaluates each
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- Worst performers are eliminated (selection pressure)
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- [Optional] New agents created by combining prompts of survivors (crossover)
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- Process repeats until satisfactory solution emerges
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## Key Insight
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- Since we can't punish or threaten LLM agents, we simply delete underperformers
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- This mirrors "survival of the fittest" in biological evolution
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- No attachment to individual agents — treat them as cattle, not pets
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## Related Concepts
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- [[Fitness Function]]
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- [[Multi-Agent Knock-out]]
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- [[Cattle vs Pets]] |