Advertisement

Fitness Landscapes and Inductive Genetic Programming

  • V. Slavov
  • N. I. Nikolaev

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.

Keywords

Decision Tree Fitness Function Mutation Operator Landscape Structure Fitness Landscape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J. Holland. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, 1992.Google Scholar
  2. [2]
    J. Horn and D.E. Goldberg. Genetic Algorithm Difficulty and the Modality of Fitness Landscapes, pages 243–269. Morgan Kaufmann Publishers, CA, 1995.Google Scholar
  3. [3]
    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.Google Scholar
  4. [4]
    J.R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, 1992.MATHGoogle Scholar
  5. [5]
    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.Google Scholar
  6. [6]
    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.Google Scholar
  7. [7]
    J.R. Quinlan. Induction of decision trees. Machine Learning, 1(1):81–106, 1986.Google Scholar
  8. [8]
    J.R. Quinlan. MDL and categorical theories (continued). In Proc. Int. Conference on Machine Learning, ICML-95, Tahoe City, CA, 1995.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • V. Slavov
    • 1
  • N. I. Nikolaev
    • 2
  1. 1.Information Technologies LabNew Bulgarian UniversitySofiaBulgaria
  2. 2.Department of Computer ScienceAmerican University in BulgariaBlagoevgradBulgaria

Personalised recommendations