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Coevolving memetic algorithms: a review and progress report.

Jim E Smith1

  • 1Faculty of Computing, Engineering, and Mathematical Sciences, University of the West of England, BS16 12QY Bristol, U.K. james.smith@uwe.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2007
PubMed
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Coevolving memetic algorithms adapt local search (LS) alongside solutions, outperforming traditional methods. This metalearning approach creates highly scalable algorithms by exploiting problem structures.

Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Metaheuristic Optimization

Background:

  • Coevolving memetic algorithms (CMAs) integrate local search (LS) with evolutionary systems.
  • CMAs demonstrate superior performance over nonadaptive algorithms on various problems.
  • Recent advancements focus on adaptive memetic algorithms.

Purpose of the Study:

  • To provide a rationale for coevolving memetic algorithms.
  • To propose a general structure for evolving LS algorithms with candidate solutions.
  • To explore flexible representations for enhanced algorithm performance.

Main Methods:

  • Developing a hybrid evolutionary system with coadapted LS algorithms and solutions.
  • Implementing a general structure for evolving populations of LS algorithms.

Related Experiment Videos

  • Utilizing rule-based representations for LS within the coevolutionary framework.

    • Main Results:

    • CMAs effectively discover and exploit problem structures and regularities.
    • The metalearning capability of CMAs leads to highly scalable algorithms.
    • New results show significant improvements with more flexible LS representations.

    Conclusions:

    • Coevolving memetic algorithms offer a powerful approach to metaheuristic search.
    • Adaptive and flexible representations are key to enhancing CMA performance.
    • Further research directions include refining representations and exploring new coevolutionary models.