Nailah AL-Madi
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.
- A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. SpringerVerlag, 2003. Google Scholar
Digital Library
- 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. Google ScholarDigital Library
- I. Witten, E. Frank, and M. Hall. Data Mining: Practical Machine Learning Tools And Techniques, 3rd Edition. Morgan Kaufmann, 2011. Google ScholarDigital Library
- K. Meffert et al., JGAP - java genetic algorithms and genetic programming package {online}. available: http://jgap.sf.net, January 2012.Google Scholar
- A. Frank and A. Asuncion. UCI machine learning repository, available: http://archive.ics.uci.edu/ml, 2010.Google Scholar
Index Terms
- Segment-based genetic programming
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