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.
Cite
CITATION STYLE
Folino, G., Pizzuti, C., & Spezzano, G. (2000). Genetic programming and simulated annealing: A hybrid method to evolve decision trees. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1802, pp. 294–303). Springer Verlag. https://doi.org/10.1007/978-3-540-46239-2_22
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