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Multi-objective evolutionary algorithms based fuzzy optimization

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 22 Jun 2004Publisher:IEEEJournal:SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), volume 1, pages 1-7
Authors: Gracia Sánchez; Fernando Jiménez; Antonio Fernandez Gómez-Skarmeta;

doi: 10.1109/icsmc.2003.1243783

Multi-objective evolutionary algorithms based fuzzy optimization

Abstract

We deal with nonlinear programming problems with fuzzy constrains for which we use parametric programming based solution methods. We propose a multi-objective approach to solve parametric programming problems, and a multi-objective Pareto-based Evolutionary Algorithm is used. Results with linear and nonlinear test problems studied in literature are shown.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
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