Abstract
Optimization Problems represent a topic whose importance is getting higher and higher for many statistical methodologies. This is particularly true for Data Mining. It is a fact that, for a particular class of problems, it is not feasible to exhaustively examine all possible solutions. This has led researchers’ attention towards a particular class of algorithms called Heuristics. Some of these Heuristics (in particular Genetic Algorithms and Ant Colony Optimization Algorithms), which are inspired to natural phenomena, have captured the attention of the scientific community in many fields. In this paper Evolutionary Algorithms are presented, in order to face two well-known problems that affect Classification and Regression Trees.
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Mola, F., Miele, R. (2006). Evolutionary Algorithms for Classification and Regression Trees. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_29
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DOI: https://doi.org/10.1007/3-540-35978-8_29
Publisher Name: Springer, Berlin, Heidelberg
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