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Surrogate-assisted evolutionary algorithms (SAEAs) hold significantimportance in resolving expensive optimization problems~(EOPs). Extensiveefforts have been devoted to improving the efficacy of SAEAs through thedevelopment of proficient model-assisted selection methods. However, generatinghigh-quality solutions is a prerequisite for selection. The fundamentalparadigm of evaluating a limited number of solutions in each generation withinSAEAs reduces the variance of adjacent populations, thus impacting the qualityof offspring solutions. This is a frequently encountered issue, yet it has notgained widespread attention. This paper presents a framework using unevaluatedsolutions to enhance the efficiency of SAEAs. The surrogate model is employedto identify high-quality solutions for direct generation of new solutionswithout evaluation. To ensure dependable selection, we have introduced twotailored relation models for the selection of the optimal solution and theunevaluated population. A comprehensive experimental analysis is performed ontwo test suites, which showcases the superiority of the relation model overregression and classification models in the selection phase. Furthermore, thesurrogate-selected unevaluated solutions with high potential have been shown tosignificantly enhance the efficiency of the algorithm.

Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization