Lossless fitness inheritance in genetic algorithms for decision trees
<|reference_start|>Lossless fitness inheritance in genetic algorithms for decision trees: 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.<|reference_end|>
@article{kalles2006lossless,
    title={Lossless fitness inheritance in genetic algorithms for decision trees},
    author={Dimitris Kalles, Athanassios Papagelis},
    journal={arXiv preprint arXiv:cs/0611166},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0611166},
    primaryClass={cs.AI cs.DS cs.NE}
}