Evade: Evolvability-guided Algorithmic Discovery

Large language models (LLMs) are increasingly used as mutation operators in evolutionary search, enabling the discovery of novel algorithms for problems in mathematics, computer systems, and other domains. Yet existing LLM-based frameworks largely inherit the classical evolutionary paradigm of selecting candidates for mutation based on quality and diversity. These criteria capture the current state of individual candidates—such as their fitness (quality) or how they differ from other selected candidates (diversity)—but do not capture their potential for further evolution. We introduce evolvability as a search signal: a candidate's capacity to generate offspring that improve fitness or exhibit behaviors that differ from those of its ancestors. We then propose Evade, which uses the estimated evolvability score to guide candidate selection, exploration–exploitation, and mutation context. Across mathematical and systems optimization problems, Evade reduces redundant search and achieves the best mean and best solution on most tasks. Case studies demonstrate Evade's ability to escape from poor solutions and discover an algorithm that surpasses the human state of the art on a real-world cloud data transfer task. Our code is available for reproducibility.