Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables. This EDA uses the multidimensional Bayesian network as its probabilistic model. In this way, it can capture the dependencies between objectives, vari...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 18; no. 4; pp. 519 - 542
Main Authors:	Karshenas, Hossein, Santana, Roberto, Bielza, Concha, Larranaga, Pedro
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.08.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Approximation algorithms Bayes methods Bayesian analysis Computer science; control theory; systems Decision theory. Utility theory Estimation Estimation of distribution algorithm Evolutionary algorithms Exact sciences and technology Information retrieval. Graph Joint objective-variable modeling Joints Many-objective problem Mathematical models Multi-objective optimization Networks Objectives Objectives relationship Operational research and scientific management Operational research. Management science Optimization Pareto optimum Probabilistic logic Probabilistic methods Probability theory Search problems Theoretical computing multiobjective optimization joint objective-variable modeling many-objective problem Estimation of distribution algorithm objectives relationship Probabilistic approach Pareto optimum Dependability Hierarchical classification Evolutionary algorithm Competitiveness Multiobjective programming Approximation algorithm Modeling Standards Optimization Experimental result Genetic algorithm Probabilistic net Bayes network Graph method
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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