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Related Experiment Videos

A genetic algorithm with disruptive selection.

T Kuo1, S Y Hwang

  • 1Inst. of Comput. Sci. & Inf. Eng., Nat. Chiao Tung Univ., Hsinchu.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1996
PubMed
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Disruptive selection, a novel method for genetic algorithms (GAs), favors both superior and inferior individuals, unlike traditional approaches. This technique enhances optimization for challenging problems, including GA-deceptive functions.

Area of Science:

  • Computational Intelligence
  • Evolutionary Computation

Background:

  • Genetic algorithms (GAs) employ population genetics principles for adaptive search.
  • Traditional GAs use 'survival-of-the-fittest,' prioritizing above-average solutions.
  • Above-average schema sampling doesn't guarantee global optimum convergence, especially with isolated peaks or high variance.

Purpose of the Study:

  • Introduce a novel selection method, disruptive selection, for genetic algorithms.
  • Address limitations of traditional monotonic fitness functions in GAs.
  • Improve GA performance on difficult optimization problems, including deceptive functions.

Main Methods:

  • Developed a disruptive selection method with a nonmonotonic fitness function.
  • Compared disruptive selection against traditional directional selection in GAs.

Related Experiment Videos

  • Conducted convergence analysis for deceptive functions.

    • Main Results:

    • GAs with disruptive selection effectively optimize functions difficult for traditional GAs.
    • Disruptive selection identified optimal solutions more reliably and quickly in some cases.
    • Convergence analysis provided insights into optima occurrence ratios for deceptive functions.

    Conclusions:

    • Disruptive selection offers an effective alternative to traditional selection methods in GAs.
    • This method is particularly beneficial for problems with high schema variance or GA-deceptiveness.
    • Disruptive selection enhances the reliability and speed of finding optima in complex search spaces.