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A Peer-to-Peer Approach to Genetic Programming

  • Juan Luis Jiménez Laredo
  • Daniel Lombraña González
  • Francisco Fernández de Vega
  • Maribel García Arenas
  • Juan Julián Merelo Guervós
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6621)

Abstract

This paper proposes a fine-grained parallelization of the Genetic Programming paradigm (GP) using the Evolvable Agent model (EvAg) The algorithm is decentralized in order to take full-advantage of a massively parallel Peer-to-Peer infrastructure. In this context, GP is particularly demanding due to its high requirements of computational power. To assess the viability of the approach, the EvAg model has been empirically analyzed in a simulated Peer-to-Peer environment where experiments were conducted on two well-known GP problems. Results show that the spatially structured nature of the algorithm is able to yield a good quality in the solutions. Additionally, parallelization improves times to solution by several orders of magnitude.

Keywords

Particle Swarm Optimization Genetic Program Canonical Approach Complex Network Structure Thread Loop 
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 2011

Authors and Affiliations

  • Juan Luis Jiménez Laredo
    • 1
  • Daniel Lombraña González
    • 2
  • Francisco Fernández de Vega
    • 2
  • Maribel García Arenas
    • 1
  • Juan Julián Merelo Guervós
    • 1
  1. 1.ATC-ETSIITUniversity of GranadaGranadaSpain
  2. 2.University of ExtremaduraMéridaSpain

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