arXiv AI
Sep 10

An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models

The paper introduces LLM-EBG, an evolutionary framework that uses a large language model as a generative operator to automatically create optimization benchmarks. By generating unconstrained single-objective continuous minimization problems expressed as mathematical formulas, the framework can produce benchmarks that consistently favor a target algorithm over a comparison algorithm in over 80% of trials. Landscape analysis shows that these generated problems exhibit distinct geometric traits, such as sensitivity to variable scaling, reflecting the search behaviors of different optimization methods.

By Yuhiro Ono, Tomohiro Harada, Yukiya Miura