Can I speed up multi-agent genetic programming?
Enhancing Multiagent Genetic Network Programming Performance Using Search Space Reduction
This paper enhances multi-agent Genetic Network Programming (GNP) by reducing its search space. It uses simplified evolutionary operators that focus on actively used parts of the agent's decision-making graph, similar to pruning infrequently used paths. This improves performance, particularly in complex scenarios.
The key improvement relevant to LLM-based multi-agent systems is the idea of dynamic search space reduction based on actual agent behavior. This could potentially be applied to LLM agents by focusing training and refinement on the parts of the LLM's "decision space" that are frequently activated during agent interactions. This could lead to more efficient use of computational resources and potentially better emergent behavior in multi-agent LLM applications.