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Analysis of Evolutionary Diversity Optimization for Permutation Problems

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dc.contributor.authorDo, A.V.
dc.contributor.authorGuo, M.
dc.contributor.authorNeumann, A.
dc.contributor.authorNeumann, F.
dc.contributor.conferenceGenetic and Evolutionary Computation Conference (GECCO) (10 Jul 2021 - 14 Jul 2021 : Lille, France - Virtual Online)
dc.contributor.editorChicano, F.
dc.contributor.editorKrawiec, K.
dc.date.issued2021
dc.description.abstractGenerating 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.
dc.description.statementofresponsibilityAnh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann
dc.identifier.citationProceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021), 2021 / Chicano, F., Krawiec, K. (ed./s), pp.574-582
dc.identifier.doi10.1145/3449639.3459313
dc.identifier.isbn9781450383509
dc.identifier.orcidGuo, M. [0000-0002-3478-9201]
dc.identifier.orcidNeumann, A. [0000-0002-0036-4782]
dc.identifier.urihttps://hdl.handle.net/2440/134975
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.publisher.placeNew York, NY, United States
dc.relation.granthttp://purl.org/au-research/grants/arc/DP190103894
dc.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
dc.source.urihttps://dl.acm.org/doi/proceedings/10.1145/3449639
dc.subjectEvolutionary algorithms; diversity maximization; traveling salesperson problem; quadratic assignment problem; run-time analysis
dc.titleAnalysis of Evolutionary Diversity Optimization for Permutation Problems
dc.typeConference paper
pubs.publication-statusPublished

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