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Adaptive non-uniform mutation for genetic algorithms

  • André Neubauer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1226)

Abstract

A theoretical analysis of Michalewicz' non-uniform mutation operator is presented and a novel variant — the adaptive non-uniform mutation operator — is proposed. The non-uniform mutation operator was developed by Michalewicz' for his modified variant of genetic algorithms modGA to tackle numerical parameter optimization problems. As is shown by mathematical analysis, this mutation operator prefers parameter values in the center of the corresponding feasible region. This leads to problems if the optimum is situated near the feasible region's boundaries. In order to avoid this undesirable tendency, the adaptive non-uniform mutation operator is proposed, the development of which rests on the mathematical analysis. Experimental results for a standard numerical parameter optimization problem are given that illustrate the superiority and effectiveness of this novel mutation operator for genetic algorithms.

Keywords

Genetic Algorithm Probability Density Function Mutation Operator Genetic Operator Arithmetical Crossover 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • André Neubauer
    • 1
  1. 1.Department of Communication EngineeringDuisburg Gerhard-Mercator-UniversityDuisburgGermany

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