Inductive Genetic Programming with Decision Trees
Abstract
This paper proposes an empirical study of inductive Genetic Programming with Decision Trees. An approach to development of fitness functions for efficient navigation of the search process is presented. It relies on analysis of the fitness landscape structure and suggests measuring its characteristics with statistical correlations. We demonstrate that this approach increases the global landscape correlation, and thus leads to mitigation of the search difficulties. Another claim is that the elaborated fitness functions help to produce decision trees with low syntactic complexity and high predictive accuracy.
Cite
Text
Nikolaev and Slavov. "Inductive Genetic Programming with Decision Trees." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_83Markdown
[Nikolaev and Slavov. "Inductive Genetic Programming with Decision Trees." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/nikolaev1997ecml-inductive/) doi:10.1007/3-540-62858-4_83BibTeX
@inproceedings{nikolaev1997ecml-inductive,
title = {{Inductive Genetic Programming with Decision Trees}},
author = {Nikolaev, Nikolay I. and Slavov, Vanio},
booktitle = {European Conference on Machine Learning},
year = {1997},
pages = {183-190},
doi = {10.1007/3-540-62858-4_83},
url = {https://mlanthology.org/ecmlpkdd/1997/nikolaev1997ecml-inductive/}
}