research-article

To adapt or not to adapt, or the beauty of random settings

Authors:

João E. Batista,

Nuno M. Rodrigues,

Sara SilvaAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 336 - 337

https://doi.org/10.1145/3319619.3321994

Published: 13 July 2019 Publication History

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Abstract

This work concerns the automatic adaptation of the probabilities of occurrence of the genetic operators in Genetic Programming. We experiment with different adaptation methods, different types of problems, and different tree-based Genetic Programming flavors with a variable number of genetic operators. Based on the published literature and on our own results, we claim that operator probabilities should be neither fixed nor carefully adapted, but instead they should be constantly and randomly changed during the evolution.

References

[1]

N. Al-Madi and S. A. Ludwig. 2012. Adaptive genetic programming applied to classification in data mining. In Proceedings of NaBIC 2012. IEEE Press, 79--85. https://doi.org/

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[2]

Lawrence Davis. 1989. Adapting Operator Probabilities in Genetic Algorithms. In Proceedings of ICGA 1989. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 61--69. http://dl.acm.org/citation.cfm?id=645512.657242

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[3]

Jeannie Fitzgerald and Conor Ryan. 2013. Individualized self-adaptive genetic operators with adaptive selection in Genetic Programming. In Proceedings of NaBIC 2013. IEEE Press, 232--237. https://doi.org/

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[4]

Luis Muñoz, Sara Silva, and Leonardo Trujillo. 2015. M3GP: Multiclass Classification with GP. In Proceedings of EuroGP 2015 (LNCS), Vol. 9025. Springer, Copenhagen, 78--91. https://doi.org/

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Index Terms

To adapt or not to adapt, or the beauty of random settings
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic programming

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Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 13 July 2019

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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References

Index Terms

Recommendations

To Adapt or Not to Adapt?: Technical Debt and Learning Driven Self-Adaptation for Managing Runtime Performance

Co-Adapt: Continuously Tailored Software

Adapt-UI: an IDE supporting model-driven development of self-adaptive UIs

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