Towards an optimal restart strategy for genetic programming

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Towards an optimal restart strategy for genetic programming

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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: 1757 - 1757

Year of Publication: 2007

ISBN:978-1-59593-697-4

Authors

Michael Solano	Oklahoma State University, Tulsa, OK
Istvan Jonyer	Oklahoma State University, Tulsa, OK

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	Anikó Ekárt , Sandor Z. Németh, Maintaining the Diversity of Genetic Programs, Proceedings of the 5th European Conference on Genetic Programming, p.162-171, April 03-05, 2002
	2	David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
	3	Marco Tomassini , Leonardo Vanneschi , Philippe Collard , Manuel Clergue, A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming, Evolutionary Computation, v.13 n.2, p.213-239, June 2005 [doi>10.1162/1063656054088549]