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Variable size population for dynamic optimization with genetic programming

Authors:

Leonardo Vanneschi,

Giuseppe CuccuAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 1895 - 1896

https://doi.org/10.1145/1569901.1570222

Published: 08 July 2009 Publication History

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Abstract

A new model of Genetic Programming with variable size population is presented in this paper and applied to the reconstruction of target functions in dynamic environments (i.e. problems where target functions change with time). The suitability of this model is tested on a set of benchmarks based on some well known symbolic regression problems. Experimental results confirm that our variable size population model finds solutions of the same quality as the ones found by standard Genetic Programming, but with a smaller amount of computational effort.

References

[1]

J. Branke. Evolutionary approaches to dynamic optimization problems -- introduction and recent trends. In J. Branke, editor, GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pages 2--4, 2003.

Google Scholar

[2]

C. Fernandes, V. Ramos, and A. Rosa. Varying the population size of artificial foraging swarms on time varying landscapes. In International Conference on Artificial Neural Networks: Biological Inspirations, volume 3696 of LNCS, pages 311--316. Springer, 2005.

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[3]

M. Keijzer. Improving symbolic regression with interval arithmetic and linear scaling. In C. Ryan et al., editor, Genetic Programming, Proceedings of the 6th European Conference, EuroGP 2003, volume 2610 of LNCS, pages 71--83, Essex, 2003. Springer, Berlin, Heidelberg, New York.

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[4]

M. Tomassini, L. Vanneschi, J. Cuendet, and F. Fernández. A new technique for dynamic size populations in genetic programming. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation (CEC'04), pages 486--493, Portland, Oregon, USA, 2004. IEEE Press, Piscataway, NJ.

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Castelli MManzoni LSilva SVanneschi LPopovič A(2017)The influence of population size in geometric semantic GPSwarm and Evolutionary Computation10.1016/j.swevo.2016.05.00432(110-120)Online publication date: Feb-2017
https://doi.org/10.1016/j.swevo.2016.05.004
Hosseini SNemati A(2015)Application of Genetic Programming for Electrical Engineering Predictive Modeling: A ReviewHandbook of Genetic Programming Applications10.1007/978-3-319-20883-1_6(141-154)Online publication date: 2015
https://doi.org/10.1007/978-3-319-20883-1_6
McDermott JWhite DLuke SManzoni LCastelli MVanneschi LJaskowski WKrawiec KHarper RDe Jong KO'Reilly UMoore J(2012)Genetic programming needs better benchmarksProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330273(791-798)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330273
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Information

Published In

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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GECCO09

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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

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Castelli MManzoni LSilva SVanneschi LPopovič A(2017)The influence of population size in geometric semantic GPSwarm and Evolutionary Computation10.1016/j.swevo.2016.05.00432(110-120)Online publication date: Feb-2017
https://doi.org/10.1016/j.swevo.2016.05.004
Hosseini SNemati A(2015)Application of Genetic Programming for Electrical Engineering Predictive Modeling: A ReviewHandbook of Genetic Programming Applications10.1007/978-3-319-20883-1_6(141-154)Online publication date: 2015
https://doi.org/10.1007/978-3-319-20883-1_6
McDermott JWhite DLuke SManzoni LCastelli MVanneschi LJaskowski WKrawiec KHarper RDe Jong KO'Reilly UMoore J(2012)Genetic programming needs better benchmarksProceedings of the 14th annual conference on Genetic and evolutionary computation10.1145/2330163.2330273(791-798)Online publication date: 7-Jul-2012
https://dl.acm.org/doi/10.1145/2330163.2330273
O'Neill MVanneschi LGustafson SBanzhaf W(2010)Open issues in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-010-9113-211:3-4(339-363)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1007/s10710-010-9113-2
Poli RVanneschi LLangdon WMcphee N(2010)Theoretical results in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-010-9110-511:3-4(285-320)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.1007/s10710-010-9110-5

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