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Related Experiment Videos

Classification of adaptive memetic algorithms: a comparative study.

Yew-Soon Ong1, Meng-Hiot Lim, Ning Zhu

  • 1School of Computer Engineering, Nanyang Technological University, Singapore 639798. asysong@ntu.edu.sg

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 14, 2006
PubMed
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Adaptive memetic algorithms (MAs) self-configure by adapting their local search methods (memes). Global-level adaptation in these evolutionary computations shows superior search performance on continuous benchmark problems.

Area of Science:

  • Evolutionary Computations
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Parameter and operator adaptation is a key research area in evolutionary computations.
  • Adaptive memetic algorithms (MAs) are hybrid evolutionary algorithms that self-configure.
  • The selection of local search methods (memes) significantly impacts MA performance.

Purpose of the Study:

  • To classify meme adaptation mechanisms in adaptive MAs.
  • To analyze the asymptotic convergence properties of adaptive MAs based on the classification.
  • To empirically evaluate different meme adaptation strategies.

Main Methods:

  • Classification of meme adaptation based on mechanism and historical knowledge.
  • Asymptotic convergence analysis of adaptive MAs.

Related Experiment Videos

  • Empirical studies on continuous benchmark problems using representative adaptive MAs.

    • Main Results:

    • A novel classification of meme adaptation strategies in adaptive MAs is presented.
    • Global-level adaptive MAs demonstrate superior search performance compared to other strategies.
    • The study provides insights into the convergence properties of different adaptive MAs.

    Conclusions:

    • Global-level adaptive MAs offer enhanced performance for continuous optimization problems.
    • The classification provides a framework for understanding and developing adaptive MAs.
    • Future research directions include exploring novel adaptation mechanisms and theoretical analyses.