Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
This work addresses a common but overlooked issue in SAEAs for expensive optimization problems, offering an incremental improvement in algorithm efficiency.
The paper tackles the problem of limited solution evaluation in surrogate-assisted evolutionary algorithms (SAEAs) for expensive optimization by introducing a framework that uses unevaluated solutions to enhance efficiency, showing that the relation model outperforms regression and classification models in selection and significantly improves algorithm efficiency.
Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving the efficacy of SAEAs through the development of proficient model-assisted selection methods. However, generating high-quality solutions is a prerequisite for selection. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thus impacting the quality of offspring solutions. This is a frequently encountered issue, yet it has not gained widespread attention. This paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models for the selection of the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.