Multi-objective techniques in genetic programming for evolving classifier systems

Parrott, D, Li, X and Ciesielski, V 2005, 'Multi-objective techniques in genetic programming for evolving classifier systems', in D Corne et al. (ed.) Proceedings of 2005 IEEE Congress on Evolutionary Computation, Vol. 2, Edinburgh, Scotland, 2-5 September 2005.


Document type: Conference Paper
Collection: Conference Papers

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Title Multi-objective techniques in genetic programming for evolving classifier systems
Author(s) Parrott, D
Li, X
Ciesielski, V
Year 2005
Conference name Congress on Evolutionary Computation
Conference location Edinburgh, Scotland
Conference dates 2-5 September 2005
Proceedings title Proceedings of 2005 IEEE Congress on Evolutionary Computation, Vol. 2
Editor(s) D Corne et al.
Publisher IEEE
Place of publication Piscataway, USA
Abstract The application of multi-objective evolutionary computation techniques to the genetic programming of classifiers has the potential to both improve the accuracy and decrease the training time of the classifiers.The performance of two such algorithms are investigated on the even 6-parity problem and the Wisconsin Breast Cancer, Iris and Wine data sets from the UCI repository. The first method explores the addition of an explicit size objective as a parsimony enforcement technique. The second represents a program¿s classification accuracy on each class as a separate objective. Both techniques give a lower error rate with less computational cost than was achieved using a standard GP with the same parameters.
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1109/CEC.2005.1554819
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