Competent genetic algorithms for weighing matrices

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dc.contributor.author Kotsireas, IS en
dc.contributor.author Koukouvinos, C en
dc.contributor.author Pardalos, PM en
dc.contributor.author Simos, DE en
dc.date.accessioned 2014-03-01T11:46:28Z
dc.date.available 2014-03-01T11:46:28Z
dc.date.issued 2011 en
dc.identifier.issn 13826905 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/37927
dc.subject Competent metaheuristics en
dc.subject Fast messy genetic algorithm en
dc.subject Messy genetic algorithm en
dc.subject Optimization en
dc.subject Weighing matrices en
dc.title Competent genetic algorithms for weighing matrices en
heal.type other en
heal.identifier.primary 10.1007/s10878-011-9404-4 en
heal.identifier.secondary http://dx.doi.org/10.1007/s10878-011-9404-4 en
heal.publicationDate 2011 en
heal.abstract In this paper, we demonstrate that the search for weighing matrices constructed from two circulants can be viewed as a minimization problem together with two competent genetic algorithms to locate optima of an objective function. The motivation to deal with the messy genetic algorithm (mGA) is given from the pioneering results of Goldberg, regarding the ability of the mGA to put tight genes together in a solution which points directly to structural patterns in weighing matrices. In order to take into advantage certain properties of two ternary sequences with zero autocorrelation we use an adaptation of the fast messy GA (fmGA) where we combine mGA with advanced techniques, such as thresholding and tie-breaking. This transformation of the weighing matrices problem to an instance of a combinatorial optimization problem seems to be promising, since we resolved two open cases for weighing matrices as these are listed in the second edition of the Handbook of Combinatorial Designs. © 2011 Springer Science+Business Media, LLC. en
heal.journalName Journal of Combinatorial Optimization en
dc.identifier.doi 10.1007/s10878-011-9404-4 en
dc.identifier.spage 1 en
dc.identifier.epage 18 en

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