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A General Framework for Statistical Performance Comparison of Evolutionary Computation Algorithms

• David Shilane, Division of Biostatistics, School of Public Health, University of California, Berkeley
• Jarno Martikainen, Power Electronics Laboratory, Helsinki University of Technology, Espoo, Finland
• Sandrine Dudoit, Division of Biostatistics, School of Public Health, University of California, Berkeley
• Seppo Ovaska, Power Electronics Laboratory, Helsinki University of Technology, Espoo, Finland

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Article comments:

Published 2006 in Proceedings, Artificial Intelligence and Applications conference 2006, Innsbruck, Austria.

Abstract:

This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A two-fold 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.

Subject Area:

Design of Experiments and Sample Surveys

Suggested Citation:

David Shilane, Jarno Martikainen, Sandrine Dudoit, and Seppo Ovaska, "A General Framework for Statistical Performance Comparison of Evolutionary Computation Algorithms" (March 2006). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 204.
http://www.bepress.com/ucbbiostat/paper204