Relation Reasoning with LLMs In Expensive Optimization

Abstract:Expensive optimization issues (EOPs) are black-field duties with costly goal evaluations and no gradient access, making the evaluation budget the important thing bottleneck. Surrogate-assisted evolutionary algorithms (SAEAs) cut back evaluations by way of surrogate predictions, however typical surrogates usually require frequent retraining as populations evolve, incurring overhead. This paper proposes R2SAEA, a reinforcement-skilled relation-based mostly massive language mannequin (LLM) surrogate assisted evolutionary algorithm. We forged relation-primarily based surrogate modeling as an in-context pairwise reasoning task. To allow efficient inference in evolutionary loops, we develop an anchor-based mostly iterative context building technique that reduces immediate complexity from quadratic to linear in population size, and a voting-based mostly aggregation scheme that converts predicted relations into scores for offspring selection. We additional construct an RL pipeline from evolutionary trajectories and advantageous-tune Qwen2.5 with GRPO. Experiments on single- and multi-goal benchmarks show improved relation prediction and state-of-the-art optimization performance over robust SAEA baselines and normal LLMs. Quantization also allows environment friendly edge deployment, supporting a zero-shot surrogate paradigm without per-era retraining. Code and models are available at this https URL.

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