An artificial immune network for multi-objective optimization

Lanaridis, A; Stafylopatis, A

dc.contributor.author	Lanaridis, A	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:46:40Z
dc.date.available	2014-03-01T02:46:40Z
dc.date.issued	2010	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32777
dc.subject	Computational Intelligence	en
dc.subject	Multi Objective Optimization	en
dc.subject	multiobjective optimization	en
dc.subject	pareto front	en
dc.subject.other	Artificial immune networks	en
dc.subject.other	NSGA-II	en
dc.subject.other	Pareto front	en
dc.subject.other	Cloning	en
dc.subject.other	Multiobjective optimization	en
dc.subject.other	Neural networks	en
dc.title	An artificial immune network for multi-objective optimization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-15822-3_65	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-15822-3_65	en
heal.publicationDate	2010	en
heal.abstract	This paper presents a method for approximating the Pareto front of a given function using Artificial Immune Networks. The proposed algorithm uses cloning and mutation to create local subsets of the Pareto front, and combines elements of these local fronts in a way that maximizes the diversity. The method is compared against SPEA and NSGA-II in a number of problems from the ZDT test suite, yielding satisfactory results. © 2010 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-15822-3_65	en
dc.identifier.volume	6353 LNCS	en
dc.identifier.issue	PART 2	en
dc.identifier.spage	531	en
dc.identifier.epage	536	en