EKO: Evolving Knowledge for Interpretable Expensive Multiobjective Optimization

Evolutionary algorithms and surrogate-based methods lack interpretability because they treat expensive multiobjective optimization problems as a black-box numerical search. However, real world problems have valuable but unused domain background that can guide search under scarce evaluations, including variable meanings and known mechanisms. Although large language model (LLM) optimizer can use domain background, existing methods neither inherit nor validate their reasoning, so fail to accumulate correct domain knowledge. We propose EKO, an evolutionary framework whose genetic representation is knowledge, which records reasoning and its validation outcomes in natural language. EKO implements knowledge evolution through three agents: the Selector chooses parent knowledge, the Generator produces offspring and new knowledge, and the Reflector validates this knowledge. EKO repeatedly updates reasoning to build global knowledge, delivering beyond a Pareto set an interpretable characterization of the problem landscape with supporting evidence. Since knowledge evolution is independent of variable representation, changing only the problem description in natural language avoids designing specialized operators for different variable types. Experiments on benchmarks from different domains demonstrate that EKO achieves competitive performance and adaptability while remaining interpretable. Ablation studies and knowledge audits confirm component effectiveness and knowledge traceability.