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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Folino, G., Pizzuti, C., Spezzano, G. (2000). Genetic Programming and Simulated Annealing: A Hybrid Method to Evolve Decision Trees. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds) Genetic Programming. EuroGP 2000. Lecture Notes in Computer Science, vol 1802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46239-2_22
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DOI: https://doi.org/10.1007/978-3-540-46239-2_22
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