Non-elitist Evolutionary Multi-objective Optimizers Revisited
Abstract
Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs. This paper revisits the performance of non-elitist EMOAs for bi-objective continuous optimization when using an unbounded external archive. This paper examines the performance of EMOAs with two elitist and one non-elitist environmental selections. The performance of EMOAs is evaluated on the bi-objective BBOB problem suite provided by the COCO platform. In contrast to conventional wisdom, results show that non-elitist EMOAs with particular crossover methods perform significantly well on the bi-objective BBOB problems with many decision variables when using the unbounded external archive. This paper also analyzes the properties of the non-elitist selection.
Cite
@article{arxiv.2009.14717,
title = {Non-elitist Evolutionary Multi-objective Optimizers Revisited},
author = {Ryoji Tanabe and Hisao Ishibuchi},
journal= {arXiv preprint arXiv:2009.14717},
year = {2020}
}
Comments
This is an accepted version of a paper published in the proceedings of GECCO 2019