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Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees

  • Gianluigi Folino
  • Clara Pizzuti
  • Giandomenico Spezzano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1802)

Abstract

A method for the data mining task of data classification, suitable to be implemented on massively parallel architectures, is proposed. The method combines genetic programming and simulated annealing to evolve a population of decision trees. A cellular automaton is used to realise a fine-grained parallel implementation of genetic programming through the diffusion model and the annealing schedule to decide the acceptance of a new solution. Preliminary experimental results, obtained by simulating the behaviour of the cellular automaton on a sequential machine, show significant better performances with respect to C4.5.

Keywords

Simulated Annealing Genetic Programming Cellular Automaton Data Mining Application Preliminary Experimental Result 
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 2000

Authors and Affiliations

  • Gianluigi Folino
    • 1
  • Clara Pizzuti
    • 1
  • Giandomenico Spezzano
    • 1
  1. 1.ISI-CNR, c/o DEISUniv. della CalabriaRende (CS)Italy

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