Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
396088 | Information Sciences | 2008 | 10 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
David Shilane, Jarno Martikainen, Sandrine Dudoit, Seppo J. Ovaska,