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A comparison of methods for self-adaptation in evolutionary algorithms

N Saravanan1, D B Fogel, K M Nelson

  • 1ETA Inc., Madison Heights, MI 48071, USA.

Bio Systems
|January 1, 1995
PubMed
Summary

Evolutionary algorithms optimize real-valued functions. A comparison of two methods for optimizing strategy parameters in evolutionary programming and evolution strategies suggests that the original evolution strategies approach is more effective, particularly when parameters are perturbed independently.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Evolutionary computation

Background:

  • Evolutionary algorithms (EAs) are widely used for real-valued function optimization.
  • EAs often employ a secondary optimization process for their strategy parameters.
  • Two distinct methods exist for this secondary parameter optimization.

Purpose of the Study:

  • To compare two alternative methods for second-level strategy parameter optimization in EAs.
  • To evaluate the performance of these methods on real-valued function optimization tasks.

Main Methods:

  • Comparative analysis of two strategy parameter optimization techniques within evolutionary algorithms.
  • Empirical evaluation across a series of real-valued function optimization problems.

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  • Focus on evolutionary programming and evolution strategies.

    • Main Results:

    • The study's findings indicate a preference for the optimization approach originally proposed in evolution strategies.
    • This preference was observed across various function optimization tasks.
    • Potential limitations suggest findings may be most applicable to independent parameter perturbation.

    Conclusions:

    • The original evolution strategies method for second-level parameter optimization appears advantageous.
    • Independent perturbation of parent solution parameters is a key consideration for applicability.
    • Further research may explore the impact of dependent parameter perturbation.