Multi-optimization improves genetic programming generalization ability

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Multi-optimization improves genetic programming generalization ability

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents

London, England

POSTER SESSION: Genetic programming: posters table of contents

Pages: 1759 - 1759

Year of Publication: 2007

ISBN:978-1-59593-697-4

Authors

Leonardo Vanneschi	University of Milano-Bicocca, Milan, Italy
Denis Rochat	University of Lausanne, Lausanne, Switzerland
Marco Tomassini	University of Lausanne, Lausanne, Switzerland

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	F. D. Francone, P. Nordin, and W. Banzhaf. Benchmarking the generalization capabilities of a compiling genetic programming system using sparse data sets. In J. R. Koza et al., editor, Genetic Programming: Proceedings of the first annual conference, pages 72--80. MIT Press, Cambridge, 1996.
	2	C. Gagne, M. Schoenauer, M. Sebag, and M. Tomassini. Genetic programming for kernel-based learning with co-evolving subsets selection. In T. Runarsson et al., editor, Parallel Problem Solving from Nature - PPSN IX, volume 4193 of Lecture Notes in Computer Science, pages 1008--1017, Berlin, Heidelberg, New York, 2006. Springer.
	3	Ibrahim Kushchu, An Evaluation of EvolutionaryGeneralisation in Genetic Programming, Artificial Intelligence Review, v.18 n.1, p.3-14, September 2002 [doi>10.1023/A:1016379201230]