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Using Self-Adaptable Probes for Dynamic Parameter Control of Parallel Evolutionary Algorithms

  • Xavier Bonnaire
  • María-Cristina Riff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)

Abstract

Controlling parameters during execution of parallel evolutionary algorithms is an open research area. Some recent research have already shown good results applying self-calibrating strategies. The motivation of this work is to improve the search of parallel genetic algorithms using monitoring techniques. Monitoring results guides the algorithm to take some actions based on both the search state and the values of its parameters. In this paper, we propose a parameter control architecture for parallel evolutionary algorithms, based on self-adaptable monitoring techniques. Our approach provides an efficient and low cost monitoring technique to design parameters control strategies. Moreover, it is completely independant of the implementation of the evolutionary algorithm.

Keywords

Genetic Algorithm Evolutionary Algorithm Monitoring Condition Parallel Genetic Algorithm Slave Processor 
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 2005

Authors and Affiliations

  • Xavier Bonnaire
    • 1
  • María-Cristina Riff
    • 1
  1. 1.Department of Computer ScienceUniversidad Técnica Federico Santa MaríaValparaísoChile

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