Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem

TL;DR

This paper introduces a co-evolutionary algorithm for the Traveling Thief Problem that effectively explores behavioral and structural diversity, outperforming existing diversity algorithms in generating varied high-quality solutions.

Contribution

The paper presents a novel co-evolutionary approach that simultaneously explores behavioral and structural diversity for the Traveling Thief Problem, demonstrating superior diversity results.

Findings

Achieves significantly higher diversity than baseline algorithms

Effectively explores multi-dimensional solution spaces

Enhances solution variety for complex optimization problems

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.