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Continuous Optimization by Evolving Probability Density Functions with a Two-Island Model

  • Alicia D. Benítez
  • Jorge Casillas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

The work presents a new evolutionary algorithm designed for continuous optimization. The algorithm is based on evolution of probability density functions, which focus on the most promising zones of the domain of each variable. Several mechanisms are included to self-adapt the algorithm to the feature of the problem. By means of an experimental study, we have observed that our algorithm obtains good results of precision, mainly in multimodal problems, in comparison with some state-of-the-art evolutionary methods.

Keywords

Local Search Local Optimum Basic Algorithm Memetic Algorithm Simplex Algorithm 
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 2006

Authors and Affiliations

  • Alicia D. Benítez
    • 1
  • Jorge Casillas
    • 1
  1. 1.Dept. Computer Science and Artificial IntelligenceUniversity of GranadaSpain

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