Lossless fitness inheritance in genetic algorithms for decision trees

Dimitris Kalles; Athanassios Papagelis

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Lossless fitness inheritance in genetic algorithms for decision trees

Dimitris Kalles·Athanassios Papagelis

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Abstract

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.

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Dimitris Kalles, Athanassios Papagelis. 2009-03-10. Lossless fitness inheritance in genetic algorithms for decision trees. https://arxiv.org/abs/cs/0611166

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