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Theoretical Analysis of the Confidence Interval Based Crossover for Real-Coded Genetic Algorithms

  • C. Hervás-Martínez
  • D. Ortiz-Boyer
  • N. García-Pedrajas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)

Abstract

In this paper we study some theoretical aspects of a new crossover operator for real-coded genetic algorithms based on the statistical features of the best individuals of the population. This crossover is based on defining a confidence interval for a localization estimator using the L 2 norm. From this confidence interval we obtain three parents: the localization estimator and the lower and upper limits of the confidence interval. In this paper we analyze the mean and variance of the population when this crossover is applied, studying the behavior of the distribution of the fitness of the individuals in a problem of optimization. We also make a comparison of our crossover with the crossovers BLX-α and UNDX-m, showing the robustness of our operator.

Keywords

Genetic Algorithm Crossover Operator Good Individual Population Density Function Ackley Function 
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 2002

Authors and Affiliations

  • C. Hervás-Martínez
    • 1
  • D. Ortiz-Boyer
    • 1
  • N. García-Pedrajas
    • 1
  1. 1.Department of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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