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Guiding function set selection in genetic programming based on fitness landscape analysis

Published: 06 July 2013 Publication History

Abstract

This paper attempts to provide a guideline for function set selection based on fitness landscape analysis. We used two well-known techniques, autocorrelation function and information content, to analysize the fitness landscape of each function set. We tested these methods on a large number of real-valued symbolic regression problems and the experimental results showed that there is a strong relationship between autocorrelation function value and the performance of a function set. Therefore, autocorrelation function can be used as a good indicator for selecting an appropriate function set for a problem.

References

[1]
K. Krawiec and T. Pawlak. Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators. Genetic Programming and Evolvable Machines, 14(1):31--63, 2013.
[2]
N. Q. Uy, M. O'Neill, N. X. Hoai, B. McKay, and E. G. Lopez. Semantic similarity based crossover in GP: The case for real-valued function regression. In P. Collet, editor, Evolution Artificielle, 9th International Conference, Lecture Notes in Computer Science, pages 13--24, October 2009.
[3]
V. K. Vassilev, T. C. Fogarty, and J. F. Miller. Information characteristics and the structure of landscapes. Evolutionary Computation, 8(1):31--60, Spring 2000.
[4]
E. D. Weinberger. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics, 63:325--336, 1990.

Cited By

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  • (2023)Fitness Landscape Analysis of Genetic Programming Search Spaces with Local Optima NetworksProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596305(2056-2063)Online publication date: 15-Jul-2023
  • (2023)Dynamic Grammar Pruning for Program Size Reduction in Symbolic RegressionSN Computer Science10.1007/s42979-023-01840-y4:4Online publication date: 17-May-2023
  • (2021)A Survey of Advances in Landscape Analysis for OptimisationAlgorithms10.3390/a1402004014:2(40)Online publication date: 28-Jan-2021
  • Show More Cited By

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  1. Guiding function set selection in genetic programming based on fitness landscape analysis

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    Published In

    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. fitness landscape
    2. function set
    3. genetic programming

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    GECCO '13
    Sponsor:
    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    Cited By

    View all
    • (2023)Fitness Landscape Analysis of Genetic Programming Search Spaces with Local Optima NetworksProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596305(2056-2063)Online publication date: 15-Jul-2023
    • (2023)Dynamic Grammar Pruning for Program Size Reduction in Symbolic RegressionSN Computer Science10.1007/s42979-023-01840-y4:4Online publication date: 17-May-2023
    • (2021)A Survey of Advances in Landscape Analysis for OptimisationAlgorithms10.3390/a1402004014:2(40)Online publication date: 28-Jan-2021
    • (2017)Statistical genetic programming for symbolic regressionApplied Soft Computing10.1016/j.asoc.2017.06.05060:C(447-469)Online publication date: 1-Nov-2017

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