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Towards a dynamic benchmark for genetic programming

Published: 06 July 2013 Publication History

Abstract

Following a recent call for a suite of benchmarks for genetic programming, we investigate the criteria for a meaningful dynamic benchmark for GP. We explore the design of a dynamic benchmark for symbolic regression, based on semantic distance between evaluated functions, where larger distances serve as a proxy for greater environmental change. We do not find convincing evidence that lower semantic distance is a good proxy for greater ease in adapting to a change. We conclude that due to fundamental characteristics of GP, it is difficult to come up with a single dynamic benchmark problem which is generally applicable.

References

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J. Branke. Memory enhanced evolutionary algorithms for changing optimization problems. In Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, volume 3. IEEE, 1999.
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I. Dempsey, M. O'Neill, and A. Brabazon. Adaptive trading with grammatical evolution. In Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, pages 2587--2592. IEEE, 2006.
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I. Dempsey, M. O'Neill, and A. Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Springer Verlag, 2009.
[4]
J. McDermott et al. Genetic programming needs better benchmarks. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, pages 791--798. ACM, 2012.
[5]
I. Moser and R. Chiong. Dynamic function optimization: The moving peaks benchmark. Metaheuristics for Dynamic Optimization, pages 35--59, 2013.
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N. Wagner, Z. Michalewicz, M. Khouja, and R. McGregor. Time series forecasting for dynamic environments: the dyfor genetic program model. Evolutionary Computation, IEEE Transactions on, 11(4):433--452, 2007.

Cited By

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  • (2019)Symbolic regression in dynamic scenarios with gradually changing targetsApplied Soft Computing10.1016/j.asoc.2019.10562183:COnline publication date: 1-Oct-2019
  • (2018)Enhancing Island Model Genetic Programming by Controlling Frequent TreesJournal of Artificial Intelligence and Soft Computing Research10.2478/jaiscr-2018-00249:1(51-65)Online publication date: 20-Aug-2018

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  1. Towards a dynamic benchmark for genetic programming

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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. dynamical optimization
    2. genetic programming

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    GECCO '13
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    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
    • (2019)Symbolic regression in dynamic scenarios with gradually changing targetsApplied Soft Computing10.1016/j.asoc.2019.10562183:COnline publication date: 1-Oct-2019
    • (2018)Enhancing Island Model Genetic Programming by Controlling Frequent TreesJournal of Artificial Intelligence and Soft Computing Research10.2478/jaiscr-2018-00249:1(51-65)Online publication date: 20-Aug-2018

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