Abstract
This paper proposes a study of the performance of inductive genetic programming with decision trees. The investigation concerns the influence of the fitness function, the genetic mutation operator and the categorical distribution of the examples in inductive tasks on the search process. The approach uses statistical correlations in order to clarify two aspects: the global and the local search characteristics of the structure of the fitness landscape. The work is motivated by the fact that the structure of the fitness landscape is the only information which helps to navigate in the search space of the inductive task. It was found that the analysis of the landscape structure allows tuning the landscape and increasing the exploratory power of the operator on this landscape.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
J. Holland. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, 1992.
J. Horn and D.E. Goldberg. Genetic Algorithm Difficulty and the Modality of Fitness Landscapes, pages 243–269. Morgan Kaufmann Publishers, CA, 1995.
T.C. Jones and S. Forrest. Fitness distance correlation as a measure of search difficulty for genetic algorithms. In Proc. Sixth Int. Conference on Genetic Algorithms, pages 184–192, 1995.
J.R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, 1992.
W.G. Macready, A.G. Siapas, and S.A. Kauffman. Criticality and parallelism in combinatorial optimization. Working Paper 95-06-054, Santa Fe Institute, Santa Fe, NM, 1995.
B. Manderick, M. de Weger, and P. Spiessens. The genetic algorithm and the structure of the fitness landscape. In Proc. Fourth Int. Conference on Genetic Algorithms, pages 143–150, 1991.
J.R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.
J.R. Quinlan. MDL and categorical theories (continued). In Proc. Int. Conference on Machine Learning, ICML-95, Tahoe City, CA, 1995.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Slavov, V., Nikolaev, N.I. (1998). Fitness Landscapes and Inductive Genetic Programming. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_91
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_91
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive