Segment-based genetic programming

Authors:
Nailah AL-Madi;Simone A. Ludwig
Affiliations:
North Dakota State University, Fargo, ND, USA;North Dakota State University, Fargo, ND, USA
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

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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.