Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

Yuan, B. and Gallagher, M. R. (2004). Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. In: X. Yao, E. Burke, J. A. Lozano, J. Smith, J. J. Merelo-Guervós, J. A. Bullinaria, J. Rowe, P. Tino, A. Kabán and H.-P. Schwefel, Parallel Problem Solving from Nature: PPSN VIII. The Eighth International Conference on Parallel Problem Solving from Nature, Birmingham, U.K., (172-181). 18-22 September 2004.


Author Yuan, B.
Gallagher, M. R.
Title of paper Statistical racing techniques for improved empirical evaluation of evolutionary algorithms
Conference name The Eighth International Conference on Parallel Problem Solving from Nature
Conference location Birmingham, U.K.
Conference dates 18-22 September 2004
Proceedings title Parallel Problem Solving from Nature: PPSN VIII   Check publisher's open access policy
Journal name Parallel Problem Solving From Nature - Ppsn VIII   Check publisher's open access policy
Place of Publication Berlin
Publisher Springer-Verlag
Publication Year 2004
Sub-type Fully published paper
ISBN 978-3-540-23092-2
ISSN 0302-9743
Editor X. Yao
E. Burke
J. A. Lozano
J. Smith
J. J. Merelo-Guervós
J. A. Bullinaria
J. Rowe
P. Tino
A. Kabán
H.-P. Schwefel
Volume 3242
Start page 172
End page 181
Total pages 10
Collection year 2004
Language eng
Abstract/Summary In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.
Subjects E1
280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic
700100 Computer Software and Services
Keyword Computer Science, Theory & Methods
Q-Index Code E1
Additional Notes Series: Lecture Notes in Computer Science, Vol. 3242

 
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Created: Thu, 23 Aug 2007, 19:25:18 EST