English

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.

Keywords

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

R2 v1 2026-06-25T01:18:05.507Z