Soft Computing

, Volume 16, Issue 2, pp 331–351 | Cite as

Modeling dynamics of a real-coded CHC algorithm in terms of dynamical probability distributions

Original Paper

Abstract

Some theoretical models have been proposed in the literature to predict dynamics of real-coded evolutionary algorithms. These models are often applied to study very simplified algorithms, simple real-coded functions or sometimes these make difficult to obtain quantitative measures related to algorithm performance. This paper, trying to reduce these simplifications to obtain a more useful model, proposes a model that describes the behavior of a slightly simplified version of the popular real-coded CHC in multi-peaked landscape functions. Our approach is based on predicting the shape of the search pattern by modeling the dynamics of clusters, which are formed by individuals of the population. This is performed in terms of dynamical probability distributions as a basis to estimate its averaged behavior. Within reasonable time, numerical experiments show that is possible to achieve accurate quantitative predictions in functions of up to 5D about performance measures such as average fitness, the best fitness reached or number of fitness function evaluations.

Keywords

CHC algorithm Probability distribution Real-coded optimization Algorithm dynamics 

Notes

Acknowledgments

This work is supported by the Ministerio de Ciencia e Innovación (Spain) under grant TEC2008-02754/TEC and TIN2008-05854.

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Jesús Marín
    • 1
  • Daniel Molina
    • 2
  • Francisco Herrera
    • 3
  1. 1.Department of Automatic Control (ESAII)Universitat Politècnica de Catalunya, EUETIBBarcelonaSpain
  2. 2.Department of Computer Science and EngineeringUniversity of CádizCádizSpain
  3. 3.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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