Using Fast Matrix Multiplication in Bio-Inspired Computation for Complex Optimization Problems

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dc.contributor.authorDiedrich, F.
dc.contributor.authorNeumann, F.
dc.contributor.conferenceIEEE Congress on Evolutionary Computation (2008 : Hong Kong)
dc.date.issued2008
dc.description.abstractPopulation-based search heuristics such as evolutionary algorithms or ant colony optimization have been widely used to tackle complex problems in combinatorial optimization. In many cases these problems involve the optimization of an objective function subject to a set of constraints which is very large. In this paper, we examine how population-based search heuristics can be sped up by making use of fast matrix multiplication algorithms. First, we point out that this approach is applicable to the wide class of problems which can be expressed as an Integer Linear Program (ILP). Later on, we investigate the speedup that can be gained by the proposed approach in our experimental studies for the multidimensional knapsack problem.
dc.description.statementofresponsibilityFlorian Diedrich and Frank Neumann
dc.identifier.citationProceedings of the IEEE Congress on Evolutionary Computation, 2008 (IEEE World Congress on Computational Intelligence), 1-6 June, 2008, pp. 3827-3832
dc.identifier.doi10.1109/CEC.2008.4631317
dc.identifier.isbn9781424418220
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/66761
dc.language.isoen
dc.publisherIEEE Press
dc.publisher.placeNew York
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computation
dc.rights© 2008 IEEE
dc.source.urihttps://doi.org/10.1109/cec.2008.4631317
dc.titleUsing Fast Matrix Multiplication in Bio-Inspired Computation for Complex Optimization Problems
dc.typeConference paper
pubs.publication-statusPublished

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