Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem
Neural and Evolutionary Computing
2022-07-29 v1
Abstract
Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the the literature.
Cite
@article{arxiv.2207.14036,
title = {Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem},
author = {Adel Nikfarjam and Aneta Neumann and Jakob Bossek and Frank Neumann},
journal= {arXiv preprint arXiv:2207.14036},
year = {2022}
}
Comments
To appear at PPSN 2022