The Compact Memetic Algorithm

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dc.contributor Genetic and Evolutionary Computation Conference (GECCO) <Chicago, 2003> de_DE Merz, Peter de_DE 2003-09-02 de_DE 2014-03-17T11:32:57Z 2003-09-02 de_DE 2014-03-17T11:32:57Z 2003 de_DE
dc.identifier.other 107331047 de_DE
dc.identifier.uri de_DE
dc.description.abstract Optimization by probabilistic modeling is a growing research field in evolutionary computation. An example is the compact genetic algorithm (cGA), in which the population of a genetic algorithm (GA) is represented as a probability distribution over the set of solutions. Both cGA algorithm and the order-one behavior of a simple GA with uniform crossover are operationally equivalent. The cGA is much easier to implement and requires less memory. In this paper, memetic algorithms (MAs) are investigated in which the population is replaced by a probability vector analogously to the cGA. The resulting compact memetic algorithms (cMAs) hence require less memory, are easier to implement and require fewer parameters than other MAs. It is shown that cMAs with and without additional recombination perform comparable to or better than population-based MAs on a set of benchmark instances of the unconstrained binary quadratic programming problem. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-nopod de_DE
dc.rights.uri de_DE
dc.rights.uri en
dc.subject.classification Memetischer Algorithmus de_DE
dc.subject.ddc 004 de_DE
dc.subject.other Memetic Algorithms , Compact Genetic Algorithm , Binary Quadratic Programming en
dc.title The Compact Memetic Algorithm en
dc.type ConferenceObject de_DE
utue.publikation.fachbereich Sonstige/Externe de_DE
utue.publikation.fakultaet 9 Sonstige / Externe de_DE
dcterms.DCMIType Text de_DE
utue.publikation.typ conferenceObject de_DE 904 de_DE
utue.opus.portal woma4 de_DE
utue.opus.portalzaehlung 0.00000 de_DE


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