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Solving Classification Problems Using Genetic Programming Algorithms on GPUs

  • Alberto Cano
  • Amelia Zafra
  • Sebastián Ventura
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alberto Cano
    • 1
  • Amelia Zafra
    • 1
  • Sebastián Ventura
    • 1
  1. 1.Department of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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