On the effect of environment-triggered population diversity compensation methods for memory enhanced UMDA

作者: Xingguang Peng , Fubin Zhang , Demin Xu

DOI:

关键词: Machine learningProcess (engineering)Identification (information)Artificial intelligencePopulation diversityCompensation (engineering)Compensation methodsComputer scienceEvolutionary computationDiversity (business)

摘要: This paper focuses on the effect of population diversity to environment identification-based memory scheme (EI-MMS) which heuristically compensates through storage and retrieving process historic information. We introduced several compensation measures combined them with EI-MMS based univariate marginal distribution algorithm (UMDA) from two aspects. First, a basic measure was used fight against inherent loss UMDA. Second, environment-triggered were added in sense dynamic environment. Based experimental results three test problems, dynamics corresponding UMDAs analyzed conclusions about how does affect performance environments drawn.

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