In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of crossover in 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 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 4 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of accuracy.

中文翻译：

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SHX：实编码遗传算法的搜索历史驱动交叉

在进化算法中，遗传算子迭代地生成新的后代，这些后代构成了一组具有潜在价值的搜索历史。为了提高实数编码遗传算法（RCGA）中交叉的性能，在本文中，我们建议在迭代期间以在线方式利用迄今为止缓存的搜索历史。具体来说，过去几代的幸存者个体被收集并存储在档案中以形成搜索历史。我们介绍了一个由搜索历史驱动的简单而有效的交叉模型（缩写为 SHX）。特别是，搜索历史被聚类，每个聚类都被分配了一个 SHX 分数。本质上，所提出的 SHX 是一种数据驱动的方法，它利用搜索历史在后代产生后进行后代选择。由于不需要额外的适应度评估，SHX 适用于预算有限或健康评估昂贵的任务。我们通过实验验证了 SHX 在 4 个基准函数上的有效性。定量结果表明，我们的 SHX 可以在准确性方面显着提高 RCGA 的性能。