Population-based incremental learning with associative memory for dynamic environments

Shengxiang YANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

234 Citations (Scopus)

Abstract

In recent years, interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) has grown 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 DOPs. 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, multipopulation, 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, multipopulation schemes for PBILs in different dynamic environments. © 2008 IEEE.
Original languageEnglish
Pages (from-to)542-561
Number of pages20
JournalIEEE Transactions on Evolutionary Computation
Volume12
Issue number5
Early online date4 Aug 2008
DOIs
Publication statusPublished - Oct 2008
Externally publishedYes

Bibliographical note

This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant EP/E060722/1 and Grant EP/E058884/1 and in part by the National Natural Science Foundation (NNSF) of China under Grant 60428202.

Keywords

  • Associative memory scheme
  • Dynamic optimization problems (DOPs)
  • Immune system-based genetic algorithm (ISGA)
  • Memory-enhanced genetic algorithm
  • Multipopulation scheme
  • Population-based incremental learning (PBIL)
  • Random immigrants

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