Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/134975
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Analysis of Evolutionary Diversity Optimization for Permutation Problems |
Author: | Do, A.V. Guo, M. Neumann, A. Neumann, F. |
Citation: | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021), 2021 / Chicano, F., Krawiec, K. (ed./s), pp.574-582 |
Publisher: | Association for Computing Machinery |
Publisher Place: | New York, NY, United States |
Issue Date: | 2021 |
ISBN: | 9781450383509 |
Conference Name: | Genetic and Evolutionary Computation Conference (GECCO) (10 Jul 2021 - 14 Jul 2021 : Lille, France - Virtual Online) |
Editor: | Chicano, F. Krawiec, K. |
Statement of Responsibility: | Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann |
Abstract: | Generating diverse populations of high quality solutions has gained interest as a promising extension to the traditional optimization tasks. We contribute to this line of research by studying evolutionary diversity optimization for two of the most prominent permutation problems, namely the Traveling Salesperson Problem (TSP) and Quadratic Assignment Problem (QAP). We explore the worst-case performance of a simple mutation-only evolutionary algorithm with different mutation operators, using an established diversity measure. Theoretical results show most mutation operators for both problems ensure production of maximally diverse populations of sufficiently small size within cubic expected run-time. We perform experiments on QAPLIB instances in unconstrained and constrained settings, and reveal much more optimistic practical performances. Our results should serve as a baseline for future studies. |
Keywords: | Evolutionary algorithms; diversity maximization; traveling salesperson problem; quadratic assignment problem; run-time analysis |
Rights: | © 2021 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org |
DOI: | 10.1145/3449639.3459313 |
Grant ID: | http://purl.org/au-research/grants/arc/DP190103894 |
Published version: | https://dl.acm.org/doi/proceedings/10.1145/3449639 |
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.