Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/136670
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Evolutionary diversity optimisation for the traveling thief problem |
Author: | Nikfarjam, A. Neumann, A. Neumann, F. |
Citation: | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22), 2022 / Fieldsend, J.E., Wagner, M. (ed./s), vol.abs/2204.02709, pp.749-756 |
Publisher: | Association for Computing Machinery |
Publisher Place: | New York, NY |
Issue Date: | 2022 |
ISBN: | 9781450392372 |
Conference Name: | The Genetic and Evolutionary Computation Conference (GECCO) (9 Jul 2022 - 13 Jul 2022 : virtual online) |
Editor: | Fieldsend, J.E. Wagner, M. |
Statement of Responsibility: | Adel Nikfarjam, Aneta Neumann, Frank Neumann |
Abstract: | There has been a growing interest in the evolutionary computation community to compute a diverse set of high-quality solutions for a given optimisation problem. This can provide the practitioners with invaluable information about the solution space and robustness against imperfect modelling and minor problems’ changes. It also enables the decision-makers to involve their interests and choose between various solutions. In this study, we investigate for the first time a prominent multi-component optimisation problem, namely the Traveling Thief Problem (TTP), in the context of evolutionary diversity optimisation. We introduce a bi-level evolutionary algorithm to maximise the structural diversity of the set of solutions. Moreover, we examine the inter-dependency among the components of the problem in terms of structural diversity and empirically determine the best method to obtain diversity. We also conduct a comprehensive experimental investigation to examine the introduced algorithm and compare the results to another recently introduced framework based on the use of Quality Diversity (QD). Our experimental results show a significant improvement of the QD approach in terms of structural diversity for most TTP benchmark instances. |
Keywords: | Evolutionary diversity optimisation; multi-component optimisation problems; traveling thief problem |
Rights: | © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM |
DOI: | 10.1145/3512290.3528862 |
Grant ID: | http://purl.org/au-research/grants/arc/DP190103894 http://purl.org/au-research/grants/arc/FT200100536 |
Published version: | https://dl.acm.org/doi/proceedings/10.1145/3512290.3528862 |
Appears in Collections: | Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.