MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

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Show simple item record Martí, Luis García Herrero, Jesús Berlanga de Jesús, Antonio 2021-11-04T10:39:51Z 2021-11-04T10:39:51Z 2016-12-01
dc.identifier.bibliographicCitation Martí, L., García, J., Berlanga, A. et al. MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm. J Glob Optim 66, 729–768 (2016).
dc.identifier.issn 1573-2916
dc.description.abstract The Extension Of Estimation Of Distribution Algorithms (Edas) To The Multiobjective Domain Has Led To Multi-Objective Optimization Edas (Moedas). Most Moedas Have Limited Themselves To Porting Single-Objective Edas To The Multi-Objective Domain. Although Moedas Have Proved To Be A Valid Approach, The Last Point Is An Obstacle To The Achievement Of A Significant Improvement Regarding "Standard" Multi-Objective Optimization Evolutionary Algorithms. Adapting The Model-Building Algorithm Is One Way To Achieve A Substantial Advance. Most Model-Building Schemes Used So Far By Edas Employ Off-The-Shelf Machine Learning Methods. However, The Model-Building Problem Has Particular Requirements That Those Methods Do Not Meet And Even Evade. The Focus Of This Paper Is On The Model- Building Issue And How It Has Not Been Properly Understood And Addressed By Most Moedas. We Delve Down Into The Roots Of This Matter And Hypothesize About Its Causes. To Gain A Deeper Understanding Of The Subject We Propose A Novel Algorithm Intended To Overcome The Draw-Backs Of Current Moedas. This New Algorithm Is The Multi-Objective Neural Estimation Of Distribution Algorithm (Moneda). Moneda Uses A Modified Growing Neural Gas Network For Model-Building (Mb-Gng). Mb-Gng Is A Custom-Made Clustering Algorithm That Meets The Above Demands. Thanks To Its Custom-Made Model-Building Algorithm, The Preservation Of Elite Individuals And Its Individual Replacement Scheme, Moneda Is Capable Of Scalably Solving Continuous Multi-Objective Optimization Problems. It Performs Better Than Similar Algorithms In Terms Of A Set Of Quality Indicators And Computational Resource Requirements.
dc.description.sponsorship This work has been funded in part by projects CNPq BJT 407851/2012-7, FAPERJ APQ1 211.451/2015, MINECO TEC2014-57022-C2-2-R and TEC2012-37832-C02-01.
dc.language.iso eng
dc.publisher Springer
dc.rights © 2016, Springer Science Business Media New York
dc.subject.other multi-objective optimization problems
dc.subject.other estimation of distribution algorithms
dc.subject.other model-building problems
dc.subject.other neural networks
dc.subject.other growing neural gas
dc.title MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm
dc.type article
dc.subject.eciencia Informática
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2012-37832-C02-01
dc.relation.projectID Gobierno de España. TEC2014-57022-C2-2-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 729
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 768
dc.identifier.publicationtitle Journal of Global Optimization
dc.identifier.publicationvolume 66
dc.identifier.uxxi AR/0000019315
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Informática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA)
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