LLMigrate: Large Language Models as Migration Controllers in Island-Based Evolutionary Design of Soft Robots

Abstract

In evolutionary robotics, island models promote exploration by evolving multiple subpopulations, though migration is typically governed by fixed heuristics. Meanwhile, quality-diversity (QD) methods like MAP-Elites excel in large-population regimes but struggle with small populations due to sparse descriptor space coverage. We propose an LLM-guided migration controller for island-model evolutionary design, where an LLM receives compact summaries of island states including fitness statistics and morphology information and outputs migration decisions. We use an LLM-based mutation operator as a component of the evolutionary pipeline, but focus on migration as a process-level control mechanism. Evaluated on three EvoGym benchmarks, our approach maintains substantially higher structural diversity than mutation-only evolution while achieving comparable fitness, and approaches the diversity of QD methods. These results demonstrate a favorable fitness-diversity trade-off in small-population settings, with task-dependent effects, and position LLMs as coordinators of inter-island information flow in evolutionary robotics. Our code is publicly available at: https://github.com/emirb0/evogym-migration