Clustering-Based Probabilistic Model Fitting in Estimation of Distribution Algorithms(Biocybernetics, Neurocomputing)

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概要

• 論文の詳細を見る

• 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.

• 社団法人電子情報通信学会の論文
• 2006-01-01

著者

• Ramakrishna Rudrapatna

  Gwangju Institute Of Science And Technology (gist)

• Ahn Chang

  Gwangju Institute Of Science And Technology (gist):(present Address)samsung Advanced Institute Of Te

• Ramakrishna Rudrapatna

  Gwangju Inst. Sci. And Technol. (gist) Gwangju Kor

関連論文

• Building-Block Supply in Real-Coded Genetic Algorithms : A First Step on the Population-Sizing Model(General Fundamentals and Boundaries)
• Clustering-Based Probabilistic Model Fitting in Estimation of Distribution Algorithms(Biocybernetics, Neurocomputing)
• A New Algorithm for Silhouette Detection in Volume Objects and Its Parallelization(Computer Graphics)
• Adaptive Clustering Technique Using Genetic Algorithms(Data Mining)

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