Title Hybridization of evolutionary algorithms and local search by means of a clustering method
External publication No
Means IEEE Trans Syst Man Cybern Part B Cybern
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 1.53800
SJR Impact 0.93100
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-33744512928&doi=10.1109%2fTSMCB.2005.860138&partnerID=40&md5=cd03407ede01218ebf1f0a011c826fce
Publication date 01/01/2006
ISI 000238069200005
Scopus Id 2-s2.0-33744512928
DOI 10.1109/TSMCB.2005.860138
Abstract This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are usually called hybrid algorithms. On the other hand, it is well known that the clustering process enables the creation of groups (clusters) with mutually close points that hopefully correspond to relevant regions of attraction. Local-search procedures can then be started once in every such region. This paper proposes the combination of an EA, a clustering process, and a local-search procedure to the evolutionary design of product-units neural networks. In the methodology presented, only a few individuals are subject to local optimization. Moreover, the local optimization algorithm is only applied at specific stages of the evolutionary process. Our results show a favorable performance when the regression method proposed is compared to other standard methods. © 2006 IEEE.
Keywords Neural networks; Nonlinear equations; Optimization; Regression analysis; Clustering method; Hybrid algorithms; Hybridization; Product-units networks; Evolutionary algorithms; algorithm; article; artif
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