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Segment-based genetic programming

Published: 06 July 2013 Publication History

Abstract

Genetic Programming (GP) is one of the successful evolutionary computation techniques applied to solve classification problems, by searching for the best classification model applying the fitness evaluation. The fitness evaluation process greatly impacts the overall execution time of GP and is therefore the focus of this research study. This paper proposes a segment-based GP (SegGP) technique that reduces the execution time of GP by partitioning the dataset into segments, and using the segments in the fitness evaluation process. Experiments were done using four datasets and the results show that SegGP can obtain higher or similar accuracy results in shorter execution time compared to standard GP.

References

[1]
A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. SpringerVerlag, 2003.
[2]
R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008.
[3]
I. Witten, E. Frank, and M. Hall. Data Mining: Practical Machine Learning Tools And Techniques, 3rd Edition. Morgan Kaufmann, 2011.
[4]
K. Meffert et al., JGAP - java genetic algorithms and genetic programming package {online}. available: http://jgap.sf.net, January 2012.
[5]
A. Frank and A. Asuncion. UCI machine learning repository, available: http://archive.ics.uci.edu/ml, 2010.

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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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. data classification
  2. evolutionary computation
  3. fitness evaluation
  4. genetic programming

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Conference

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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