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.
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© 1997 Springer-Verlag Berlin Heidelberg
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Nikolaev, N.I., Slavov, V. (1997). Inductive Genetic Programming with Decision Trees. In: van Someren, M., Widmer, G. (eds) Machine Learning: ECML-97. ECML 1997. Lecture Notes in Computer Science, vol 1224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62858-4_83
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DOI: https://doi.org/10.1007/3-540-62858-4_83
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