In the field of artificial intelligence, real parameter single objective optimization is an important direction. Both the Differential Evolution (DE) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) demonstrate good performance for real parameter single objective optimization. Nevertheless, there exist other types of evolutionary algorithm for the purpose. In recent years, researchers begin to study long-term search. EA4eig - an ensemble of three DE variants and CMA-ES - performs well for long-term search. In this paper, we introduce two types of evolutionary algorithm proposed recently - crisscross search and sparrow search - into EA4eig as secondary evolutionary algorithms to process inferior individuals. Thus, EA4eigCS is obtained. In our ensemble, the secondary evolutionary algorithms are expected to vary distribution of the population for breaking stagnation. Experimental results show that our EA4eigCS outperforms EA4eig and is competitive when compared with state-of-the-art algorithms. Code and supplementary material are available at:https://anonymous.4open.science/r/EA4eigCS-2A43.

翻译：在人工智能领域，实参数单目标优化是一个重要方向。差分进化算法与协方差矩阵自适应进化策略在实参数单目标优化问题上均表现出良好性能。然而，还存在其他类型的进化算法可用于此目的。近年来，研究者开始关注长期搜索问题。EA4eig——一种集成三种差分进化变体与协方差矩阵自适应进化策略的算法——在长期搜索中表现优异。本文引入两种近期提出的进化算法——交叉搜索与麻雀搜索——作为辅助进化算法集成到EA4eig中，专门处理劣质个体，从而得到EA4eigCS算法。在我们的集成框架中，辅助进化算法旨在改变种群分布以打破搜索停滞。实验结果表明，EA4eigCS优于原始EA4eig算法，且与当前最先进算法相比具有竞争力。代码及补充材料详见：https://anonymous.4open.science/r/EA4eigCS-2A43。