wiki-ingest: Multi-Agent System Reliability

wiki/concepts/Genetic-Algorithms.md

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