code-evolution

Multi-agent code evolution optimizes scheduling and packing problems by coordinating autonomous AI agents to iteratively plan, spawn, and evaluate code solutions across generations. This approach yields superior results for combinatorial optimization compared to single-solution methods.

An immutable evaluation harness is a frozen testing framework that allows autonomous AI agents to implement and test optimization strategies without altering the core evaluation logic. It ensures reproducible benchmarking across multiple evolutionary generations.

To set up a generation loop, initialize the orchestrator with your problem configuration, define agent prompts, and provide an immutable evaluation harness. The system will then autonomously spawn, evaluate, and prune code solutions across iterative generations.

Yes, multi-agent evolutionary optimization is explicitly designed for combinatorial search problems. Agents explore diverse strategies simultaneously, sharing successful ideas through cross-inspiration to discover efficient solutions where single-agent approaches might struggle.

Multi-agent evolution outperforms single-solution approaches for geometry optimization by enabling parallel exploration of the solution space. Autonomous agents cross-inspire each other and archive the best solutions, improving overall results through collaborative generations.

Yes, an immutable evaluation harness is required. It acts as a frozen benchmark so autonomous agents can safely implement diverse strategies without corrupting the evaluation logic, ensuring consistent scoring across all evolutionary generations.