期刊
INFORMATION SCIENCES
卷 178, 期 14, 页码 2870-2879出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2008.03.007
关键词
evolutionary computation; genetic algorithms; performance comparison; statistics; twofold sampling; bootstrap; multiple hypothesis testing
This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A twofold sampling scheme for collecting performance data is introduced, and these data are analyzed using bootstrap-based multiple hypothesis testing procedures. The proposed method is sufficiently flexible to allow the researcher to choose how performance is measured, does not rely upon distributional assumptions, and can be extended to analyze many other randomized numeric optimization routines. As a result, this approach offers a convenient, flexible, and reliable technique for comparing algorithms in a wide variety of applications. (C) 2008 Published by Elsevier Inc.
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