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Locally-adaptive and memetic evolutionary pattern search algorithms.

William E Hart1

  • 1Sandia National Laboratories, P.O. Box 5800, MS 1110, Albuquerque, NM 87185-1110, USA. wehart@sandia.gov

Evolutionary Computation
|June 14, 2003
PubMed
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This study enhances evolutionary pattern search algorithms (EPSAs) by introducing locally-adaptive mutation step lengths. This adaptation improves convergence theory for various optimization problems, including memetic approaches.

Area of Science:

  • Optimization and Evolutionary Computation
  • Mathematical and Computational Sciences

Background:

  • Existing evolutionary pattern search algorithms (EPSAs) exhibit limited convergence theory for unconstrained and linearly constrained problems.
  • Current EPSAs face challenges in achieving robust convergence, particularly for complex optimization landscapes.

Purpose of the Study:

  • To adapt and strengthen the convergence theory for EPSAs.
  • To introduce a novel approach, locally-adaptive EPSAs (LA-EPSAs), by allowing individual mutation step lengths.
  • To extend the applicability of enhanced convergence theory to memetic EPSAs.

Main Methods:

  • Modified EPSA framework to incorporate individual mutation step lengths for each population member.
  • Development of locally-adaptive mechanisms where mutation step lengths adapt to local neighborhoods.

Related Experiment Videos

  • Integration of standard evolutionary algorithm formulations suitable for LA-EPSAs.

    • Main Results:

    • Established an adapted convergence theory for LA-EPSAs.
    • Demonstrated that LA-EPSAs provide improved convergence properties compared to standard EPSAs.
    • Successfully applied the enhanced convergence theory to memetic EPSAs, incorporating local search refinement.

    Conclusions:

    • Locally-adaptive mutation step lengths significantly enhance the convergence theory of EPSAs.
    • LA-EPSAs offer a more robust and adaptable framework for solving a broader range of optimization problems.
    • The developed theory provides a foundation for more effective memetic evolutionary algorithms.