Show simple item record He, Jun Jansen, Thomas Zarges, Christine 2020-02-24T03:10:29Z 2020-02-24T03:10:29Z 2019-07-13
dc.identifier.citation He , J , Jansen , T & Zarges , C 2019 , Unlimited Budget Analysis . in Genetic and Evolutionary Computation Conference : Companion . Association for Computing Machinery , pp. 427-428 , GECCO 2019: The Genetic and Evolutionary Computation Conference , Prague , Czech Republic , 13 Jul 2019 . en
dc.identifier.citation conference en
dc.identifier.other PURE: 29321139
dc.identifier.other PURE UUID: 3687a9e8-140a-4304-b707-536fce31347a
dc.identifier.other ORCID: /0000-0002-2829-4296/work/61185311
dc.identifier.other ORCID: /0000-0002-1596-086X/work/61776008
dc.identifier.other Scopus: 85070623891
dc.description.abstract Performance analysis of randomised search heuristics is a rapidly growing and developing field. We contribute to its further development by introducing a novel analytical perspective that we call unlimited budget analysis. It has its roots in the very recently introduced approximation error analysis and bears some similarity to fixed budget analysis. The focus is on the progress an optimisation heuristic makes towards a set goal, not on the time it takes to reach this goal, setting it far apart from runtime analysis. We present the framework, apply it to simple mutation-based algorithms, covering both, local and global search. We provide analytical results for a number of simple example functions for unlimited budget analysis and compare them to results derived within the fixed budget framework for the same algorithms and functions. en
dc.language.iso eng
dc.publisher Association for Computing Machinery
dc.relation.ispartof Genetic and Evolutionary Computation Conference en
dc.rights en
dc.title Unlimited Budget Analysis en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/conference en
dc.description.version authorsversion en
dc.contributor.institution Department of Computer Science en

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