SHX: Search History Driven Crossover for Real-Coded Genetic Algorithm
This work addresses performance bottlenecks in evolutionary algorithms for optimization tasks with limited computational budgets, representing an incremental improvement.
The paper tackled the problem of improving crossover performance in real-coded genetic algorithms by proposing SHX, a search history-driven crossover method that uses archived survivor individuals to select offspring without extra fitness evaluations, resulting in significant accuracy enhancements on 4 benchmark functions.
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.