An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

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An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

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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 9: genetic algorithms table of contents

Pages 1879-1880

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Bo Yuan	Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
Marcus Gallagher	School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

Sponsors

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

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ACM New York, NY, USA

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ABSTRACT

Success rate is a commonly adopted performance criterion for evaluating Evolutionary Algorithms due to their inherent randomness. However, the classical large-sample binomial test based on normal distributions is only valid with a relatively large number of trials, which may not be feasible when experimental studies are very time consuming or expensive. In this paper, we give an alternative statistical test, which is suitable for situations where results from only a small number of trials are available.

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	Eiben, A. and Jelasity, M. A critical note on experimental research methodology in EC. In Proceedings of 2002 Congress on Evolutionary Computation, 2002, 582--587.
	2	McGeoch, C. Toward an experimental method for algorithm simulation. INFORMS J. Comput., 8(1), 1--15, 1996.
	3	Ronald L. Rardin , Reha Uzsoy, Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial, Journal of Heuristics, v.7 n.3, p.261-304, May 2001 [doi>10.1023/A:1011319115230]
	4	Taillard, É. A statistical test for comparing success rates. In Proceedings of 2003 Metaheuristic International Conference, 2003 (extended abstract).
	5	Taillard, É., Waelti, P. and Zuber, J. Few statistical tests for proportions comparisons. European Journal of Operational Research, 185(3), 1336--1350, 2008.