In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.

中文翻译：

---

实编码遗传算法的搜索历史驱动后代生成方法

在进化算法中，遗传算子迭代生成新的后代，这些后代构成了一组潜在的有价值的搜索历史。为了提高实编码遗传算法（RCGA）中的后代生成性能，在本文中，我们建议在迭代过程中以在线样式利用到目前为止缓存的搜索历史记录。具体而言，将过去几代的幸存者个人收集起来并存储在档案中，以形成搜索历史。我们介绍由搜索历史记录（简称为SHX）驱动的简单而有效的交叉模型。特别是，搜索历史是聚类的，并且为每个聚类分配了SHX分数。本质上，提出的SHX是一种数据驱动的方法，它利用搜索历史在后代生成后执行后代选择。由于不需要其他适应性评估，因此SHX非常适合预算有限或昂贵的适应性评估的任务。我们通过实验验证了SHX超过15种基准功能的有效性。定量结果表明，我们的SHX可以在准确性和收敛速度方面显着提高RCGA的性能。而且，与总处理时间相比，所引起的额外运行时间可以忽略不计。