MB-GNG: Addressing drawbacks in multi-objective optimization estimation of distribution algorithms

We examine the model-building issue related to multi-objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model-build...

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Veröffentlicht in:	Operations research letters Jg. 39; H. 2; S. 150 - 154
Hauptverfasser:	Martí, Luis, García, Jesús, Berlanga, Antonio, Coello Coello, Carlos A., Molina, José M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier B.V 01.03.2011 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Computer science; control theory; systems Dealing Decision theory. Utility theory Empirical analysis Estimation of distribution algorithm Exact sciences and technology Growing neural gas Mathematical programming Model building Multi-objective optimization Networks Operational research and scientific management Operational research. Management science Operations research Optimization Theoretical computing Multi-objective optimization Model building Growing neural gas Estimation of distribution algorithm Evolutionary algorithm Empirical method Multiobjective programming Neural network Modeling Optimization Mathematical programming
ISSN:	0167-6377, 1872-7468
Online-Zugang:	Volltext
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