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A Hooke-Jeeves Based Memetic Algorithm for Solving Dynamic Optimisation Problems

  • Irene Moser
  • Raymond Chiong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

Abstract

Dynamic optimisation problems are difficult to solve because they involve variables that change over time. In this paper, we present a new Hooke-Jeeves based Memetic Algorithm (HJMA) for dynamic function optimisation, and use the Moving Peaks (MP) problem as a test bed for experimentation. The results show that HJMA outperforms all previously published approaches on the three standardised benchmark scenarios of the MP problem. Some observations on the behaviour of the algorithm suggest that the original Hooke-Jeeves algorithm is surprisingly similar to the simple local search employed for this task in previous work.

Keywords

Hooke-Jeeves pattern search extremal optimisation dynamic function optimisation moving peaks problem 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Irene Moser
    • 1
  • Raymond Chiong
    • 2
  1. 1.Faculty of ICT, Swinburne University of TechnologyMelbourneAustralia
  2. 2.School of Computing & DesignSwinburne University of Technology (Sarawak Campus)Kuching, SarawakMalaysia

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