Lossless fitness inheritance in genetic algorithms for decision trees

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When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is enough for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speed-up on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide-and-conquer nature of decision trees. The theoretical results are supported by experimental evidence.
Contains 23 pages, 6 figures, 12 tables. Text last updated as of March 6, 2009. Submitted to a journal

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Artificial Intelligence, Data Structures and Algorithms, Neural and Evolutionary Computing

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http://salesiana.dossiersoluciones.com/handle/123456789/222725

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0611166
 http://arxiv.org/abs/cs/0611166

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arXiv