A Grid-Oriented Genetic Algorithm

  • J. Herrera
  • E. Huedo
  • R. S. Montero
  • I. M. Llorente
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3470)


Genetic algorithms (GAs) are stochastic search methods that have been successfully applied in many search, optimization, and machine learning problems. Their parallel counterpart (PGA, parallel genetic algorithms) offers many advantages over the traditional GAs, such as speed, ability to search on a larger search space, and less likely to run into a local optimum. With the advent of Grid computing, the computational power that can be deliver to the applications have substantially increased, and so PGAs can potentially benefit from this new Grid technologies. However, because of the dynamic and heterogeneous nature of Grid environments, the implementation and execution of PGAs in a Grid involve challenging issues. This paper discusses the distribution of a PGA across the Grid using the DRMAA standard API and the Grid Way framework. The efficiency and reliability of this schema to solve the One Max problem is analyzed in a globus-based research testbed.


Genetic Algorithm Grid Environment Parallel Genetic Algorithm Stochastic Search Method Dynamic Connectivity 
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

  • J. Herrera
    • 1
  • E. Huedo
    • 2
  • R. S. Montero
    • 1
  • I. M. Llorente
    • 1
    • 2
  1. 1.Departamento de Arquitectura de Computadores y AutomáticaUniversidad ComplutenseMadridSpain
  2. 2.Laboratorio de Computación AvanzadaSimulación y Aplicaciones Telemáticas, Centro de Astrobiología (CSIC-INTA)Torrejón de ArdozSpain

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