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

作     者：KAEDI Marjan GHASEM-AGHAEE Nasser AHN Chang Wook 

作者机构：Department of Information technology Engineering University of Isfahan Department of Computer Engineering University of Isfahan Department of Computer Engineering Sungkyunkwan University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2013年第56卷第9期

页      面：110-126页

核心收录：

学科分类：08[工学] 081201[工学-计算机系统结构] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by NRF Grant funded by the Korean government (MEST) (Grant No. 2012-013-735) MSIP, Korea under ITRC NIPA-2013 (Grant No. H0301-13-3001) 

主　　题：dynamic optimization Bayesian optimization algorithm holographic memory dynamic Bayesian networks. 

摘      要：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.