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Fitness distance correlation and Ridge functions

  • R. J. Quick
  • V. J. Rayward-Smith
  • G. D. Smith
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

Fitness Distance Correlation has been proposed as a measure of function optimization difficulty. This paper describes a class of functions, named the Ridge Functions which, according to the measure, should be highly misleading. However, all functions tested were optimized easily by both a GA and a simple hill climbing algorithm. Scatter graph analysis of Ridge functions gave little guidance due to the large number of functions with an identical scatter graph, the majority of which are not in the class of Ridge functions and are not simple to optimize.

Keywords

Genetic Algorithm Search Space Scatter Diagram Hill Climber Hill Climbing Algorithm 
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.

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References

  1. 1.
    L. Altenberg. Fitness distance correlation analysis: An instructive counterexample. In Proceedings of the Seventh International Conference on Genetic Algorithms, pages 57–64. Morgan Kaufmann, 1997.Google Scholar
  2. 2.
    D. E. Goldberg J. Horn and K. Deb. Long path problems. In Parallel Problem Solving from Nature. Springer, 1994.Google Scholar
  3. 3.
    L. Kallel and M. Schoenauer. A priori comparison of binary crossover operators: No universal statistical measure, but a set of hints. In Proceedings of the third Artificial Evolution, pages 287–299. Springer, 1997.Google Scholar
  4. 4.
    B. Naudts and L. Kallel. Some facts about so called GA-hardness measures. Technical report, University of Antwerp, 1998.Google Scholar
  5. 5.
    V. J. Rayward-Smith R. J. Quick and G. D. Smith. Ridge functions. Technical Report 98-005, University of East Anglia, 1998.Google Scholar
  6. 6.
    T.Jones. Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, Massachusetts Institute of Technology, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • R. J. Quick
    • 1
  • V. J. Rayward-Smith
    • 1
  • G. D. Smith
    • 1
  1. 1.University of East AngliaNorwichUK

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