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dc.contributor.authorAmmar, Marwa
dc.contributor.authorBouaziz, Souhir
dc.contributor.authorAlimi, Adel M.
dc.contributor.authorAbraham, Ajith
dc.date.accessioned2017-01-04T12:36:02Z
dc.date.available2017-01-04T12:36:02Z
dc.date.issued2016
dc.identifier.citationNeurocomputing. 2016, vol. 214, p. 307-316.cs
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.urihttp://hdl.handle.net/10084/116560
dc.description.abstractIn this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)' training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi -Dimensional Particle Swarm Optimization (PMD_PSO) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree (PMA_FNT). To assess the effectiveness of PMA_FNT, four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesNeurocomputingcs
dc.relation.urihttp://dx.doi.org/10.1016/j.neucom.2016.06.019cs
dc.rights© 2016 Elsevier B.V. All rights reserved.cs
dc.subjectflexible neural treecs
dc.subjectmulti-agent architecturecs
dc.subjectmulti-objective optimizationcs
dc.subjectevolutionary computation algorithmscs
dc.subjectnegotiationcs
dc.subjectclassificationcs
dc.titleMulti-agent architecture for Multi-objective optimization of Flexible Neural Treecs
dc.typearticlecs
dc.identifier.doi10.1016/j.neucom.2016.06.019
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume214cs
dc.description.lastpage316cs
dc.description.firstpage307cs
dc.identifier.wos000386741300029


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