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Holographic memory-based bayesian optimization algorithm (HM-BOA) in dynamic environments

Science China(Information Sciences) 2013年第9期56卷 110-126页

作者： KAEDI Marjan GHASEM-AGHAEE Nasser AHN Chang Wook Department of Information technology Engineering University of Isfahan Department of Computer Engineering University of Isfahan Department of Computer Engineering Sungkyunkwan University

This paper presents a new evolutionary dynamic optimization algorithm, holographic memory-based bayesian optimization algorithm (HM-BOA), whose objective is to address the weaknesses of sequential memory-based dynamic optimization approaches. To this end, holographic associative neural memory is applied to one of the recent successful memory-based evolutionary methods, DBN-MBOA (memory-based BOA with dynamic bayesian networks.. Holographic memory is appropriate for encoding environmental changes since its stimulus and response data are represented by a vector of complex numbers such that the phase and the magnitude denote the information and its confidence level, respectively. In the learning process in HM-BOA, holographic memory is trained by probabilistic models at every environmental change. Its weight matrix contains abstract information obtained from previous changes and is used for constructing a new probabilistic model when the environment changes. The unique features of HM-BOA are: 1) the stored information can be generalized, and 2) a small amount of memory is required for storing the probabilistic models. Experimental results adduce grounds for its effectiveness especially in random environments.

关键词： dynamic optimization bayesian optimization algorithm holographic memory dynamic bayesian networks.

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