Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/4594
Title: Population-based incremental learning with associative memory for dynamic environments
Authors: Yang, S
Yao, X
Keywords: Associative memory scheme;Dynamic optimization problems (DOPs);System based genetic algorithm (ISGA);Memory-enhanced genetic algorithm;Multi-population scheme;Population-based incremental learning (PBIL);Random immigrants
Issue Date: 2008
Publisher: IEEE Press
Citation: IEEE Transactions on Evolutionary Computation, 12(5): 542 - 561, Oct 2008
Abstract: In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments.
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URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4454713&tag=1
http://bura.brunel.ac.uk/handle/2438/4594
ISSN: 1089-778X
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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