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.

翻译：代理辅助进化算法（SAEAs）在解决昂贵优化问题中具有重要意义。大量研究致力于通过开发高效的模型辅助选择方法来提升SAEAs的性能，然而生成高质量解是选择的前提条件。SAEAs中每代仅评估有限数量解的基本范式会降低相邻种群的方差，从而影响子代解的质量。这一问题虽普遍存在，却未得到广泛关注。本文提出一种利用未评估解提升SAEAs效率的框架：通过代理模型识别高质量解，直接生成新解而无需评估。为确保可靠选择，我们引入了两种定制化关系模型，分别用于最优解选择与未评估种群选择。在两个测试套件上的综合实验分析表明，关系模型在选择阶段优于回归模型和分类模型。此外，经代理模型筛选的具有高潜力的未评估解被证实能显著提升算法效率。