Clustering-Based Probabilistic Model Fitting in Estimation of Distribution Algorithms

Chang Wook AHN  Rudrapatna S. RAMAKRISHNA  

IEICE TRANSACTIONS on Information and Systems   Vol.E89-D   No.1   pp.381-383
Publication Date: 2006/01/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.1.381
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Biocybernetics, Neurocomputing
evolutionary computation,  estimation of distribution algorithms,  probabilistic model fitting,  fitness,  clustering,  

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An efficient clustering strategy for estimation of distribution algorithms (EDAs) is presented. It is used for properly fitting probabilistic models that play an important role in guiding search direction. To this end, a fitness-aided ordering scheme is devised for deciding the input sequence of samples (i.e., individuals) for clustering. It can effectively categorise the individuals by using the (available) information about fitness landscape. Moreover, a virtual leader is introduced for providing a reliable reference for measuring the distance from samples to its own cluster. The proposed algorithm incorporates them within the framework of random the leader algorithm (RLA). Experimental results demonstrate that the proposed approach is more effective than the existing ones with regard to probabilistic model fitting.