  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.

Neural and Evolutionary Computing