Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions

  • Santiago Muelas
  • José-María Peña
  • Antonio LaTorre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)


The selection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Santiago Muelas
    • 1
  • José-María Peña
    • 1
  • Antonio LaTorre
    • 1
  1. 1.DATSI, Facultad de InformáticaUniversidad Politécnica de MadridSpain

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