WikiEvolve: Coupling Foundational and Experiential Knowledge in Self-Evolving Agents

Self-evolving agents can improve solutions for various tasks by iteratively proposing them based on past experience (e.g., proposed algorithms and their utilities), evaluating their utilities, and using evolutionary search to select the best solution. However, as these agents might only make minor modifications and do finite exploration, useful solutions can remain undiscovered, limiting the effectiveness of experience-based evolution. To address this limitation, we introduce WikiEvolve, a novel framework that couples a fixed foundational wiki with an accumulating experiential memory. The foundational wiki provides established foundational knowledge from literature to enrich candidate solutions, while the experiential memory records past experience to guide future selection. Across distributed constraint optimization (DCOP) and Bayesian optimization (BO) tasks, our framework usually improves the final solution utility over experience-based evolution. Moreover, our experiments show how the amount of foundational knowledge influences the final solutions. These results demonstrate that combining a structured foundational wiki with an experiential memory enables self-evolving agents to break out of trial-and-error traps and discover solutions beyond what pure experience can reach.