On the performance of baseline evolutionary algorithms on the dynamic knapsack problem

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dc.contributor.authorRoostapour, V.
dc.contributor.authorNeumann, A.
dc.contributor.authorNeumann, F.
dc.contributor.conferenceInternational Conference on Parallel Problem Solving from Nature (PPSN) (8 Sep 2018 - 12 Sep 2018 : Coimbra, Portugal)
dc.contributor.editorAuger, A.
dc.contributor.editorFonseca, C.M.
dc.contributor.editorLourenço, N.
dc.contributor.editorMachado, P.
dc.contributor.editorPaquete, L.
dc.contributor.editorWhitley, L.D.
dc.date.issued2018
dc.description.abstractEvolutionary algorithms are bio-inspired algorithms that can easily adapt to changing environments. In this paper, we study single- and multi-objective baseline evolutionary algorithms for the classical knapsack problem where the capacity of the knapsack varies over time. We establish different benchmark scenarios where the capacity changes every τ iterations according to a uniform or normal distribution. Our experimental investigations analyze the behavior of our algorithms in terms of the magnitude of changes determined by parameters of the chosen distribution, the frequency determined by τ and the class of knapsack instance under consideration. Our results show that the multi-objective approaches using a population that caters for dynamic changes have a clear advantage on many benchmarks scenarios when the frequency of changes is not too high.
dc.description.statementofresponsibilityVahid Roostapour, Aneta Neumann, and Frank Neumann
dc.identifier.citationLecture Notes in Artificial Intelligence, 2018 / Auger, A., Fonseca, C.M., Lourenço, N., Machado, P., Paquete, L., Whitley, L.D. (ed./s), vol.11101, pp.158-169
dc.identifier.doi10.1007/978-3-319-99253-2_13
dc.identifier.isbn9783319992525
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidRoostapour, V. [0000-0002-8896-9590]
dc.identifier.orcidNeumann, A. [0000-0002-0036-4782]
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/120107
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeCham, Switzerland
dc.relation.granthttp://purl.org/au-research/grants/arc/DP160102401
dc.relation.ispartofseriesLecture Notes in Computer Science; 11101
dc.rights© Springer Nature Switzerland AG 2018
dc.source.urihttps://doi.org/10.1007/978-3-319-99253-2
dc.titleOn the performance of baseline evolutionary algorithms on the dynamic knapsack problem
dc.typeConference paper
pubs.publication-statusPublished

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