论文标题
SHX：搜索历史驱动的真实遗传算法的跨界
SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm
论文作者
论文摘要
在进化算法中，遗传运营商迭代产生新的后代，这些后代构成了一组潜在的搜索历史集。为了提高实体遗传算法（RCGA）中跨界的性能，在本文中，我们建议在迭代期间以在线风格中利用搜索历史。具体而言，过去几代人的幸存者个人被收集并存储在档案中，以形成搜索历史记录。我们介绍了一个由搜索历史记录驱动的简单而有效的跨界模型（缩写为SHX）。特别是，搜索历史记录是聚类的，每个群集被为SHX分配得分。本质上，提出的SHX是一种数据驱动的方法，它利用搜索历史记录在后代生成后执行后代选择。由于不需要额外的健身评估，因此SHX对预算有限或昂贵的健身评估的任务有利。我们通过实验验证SHX在4个基准函数上的有效性。定量结果表明，我们的SHX可以显着增强RCGA的性能，从精度上。
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.
