Combinatorial optimization for weighing matrices with the ordering messy genetic algorithm

Koukouvinos, C; Simos, DE

dc.contributor.author	Koukouvinos, C	en
dc.contributor.author	Simos, DE	en
dc.date.accessioned	2014-03-01T02:47:18Z
dc.date.available	2014-03-01T02:47:18Z
dc.date.issued	2011	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33066
dc.subject	competent metaheuristics	en
dc.subject	messy genetic algorithm	en
dc.subject	optimization	en
dc.subject	ordering messy genetic algorithm	en
dc.subject	Weighing matrices	en
dc.subject.other	Circulants	en
dc.subject.other	Combinatorial design	en
dc.subject.other	Combinatorial optimization problems	en
dc.subject.other	messy genetic algorithm	en
dc.subject.other	Messy genetic algorithms	en
dc.subject.other	Meta heuristics	en
dc.subject.other	ordering messy genetic algorithm	en
dc.subject.other	Permutation problems	en
dc.subject.other	Random keys	en
dc.subject.other	Structural pattern	en
dc.subject.other	Weighing matrices	en
dc.subject.other	Combinatorial optimization	en
dc.subject.other	Genes	en
dc.subject.other	Linear transformations	en
dc.subject.other	Optimization	en
dc.subject.other	Weighing	en
dc.subject.other	Genetic algorithms	en
dc.title	Combinatorial optimization for weighing matrices with the ordering messy genetic algorithm	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-20662-7_13	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-20662-7_13	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 permutation problem. To solve it a set of two competent genetic algorithms (CGAs) are used to locate common integers in two sorted arrays. 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 a recent formalism on the support of two sequences with zero autocorrelation we use an adaptation of the ordering messy GA (OmeGA) where we combine the fast mGA with random keys to represent permutations of the two sequences under investigation. This transformation of the weighing matrices problem to an instance of a combinatorial optimization problem seems to be promising since we illustrate that our framework is capable to solve open cases for weighing matrices as these are listed in the second edition of the Handbook of Combinatorial Designs. © 2011 Springer-Verlag.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-20662-7_13	en
dc.identifier.volume	6630 LNCS	en
dc.identifier.spage	148	en
dc.identifier.epage	156	en