Bloat control in genetic programming by evaluating contribution of nodes

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Bloat control in genetic programming by evaluating contribution of nodes

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

POSTER SESSION: Track 10: genetic programming table of contents

Pages 1893-1894

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Andy Song	RMIT University, Melbourne, Australia
Dunhai Chen	RMIT University, Melbourne, Australia
Mengjie Zhang	Victoria University of Wellington, Wellington, New Zealand

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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ABSTRACT

Unnecessary growth in program size is known as bloat problem in Genetic Programming. There are a large number of studies addressing this problem. In this paper, we propose an effective bloat control mechanism which is based on examining the contribution of each function node in the selected programs. Nodes without contribution will be removed before generating offspring. The results show that the method can significantly reduce program size without compromising fitness. Furthermore it speeds up evolution processes because of the saving in evaluation costs.

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	Sean Luke , Liviu Panait, A comparison of bloat control methods for genetic programming, Evolutionary Computation, v.14 n.3, p.309-344, September 2006 [doi>10.1162/evco.2006.14.3.309]
	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. (With contributions by J. R. Koza).
	3	Andy Song , Danny Fang, Robust method of detecting moving objects in videos evolved by genetic programming, Proceedings of the 10th annual conference on Genetic and evolutionary computation, July 12-16, 2008, Atlanta, GA, USA [doi>10.1145/1389095.1389405]