A general framework for statistical performance comparison of evolutionary computation algorithms

Authors:
David Shilane;Jarno Martikainen;Sandrine Dudoit;Seppo J. Ovaska
Affiliations:
University of California, Berkeley, Division of Biostatistics, Berkeley, CA;Helsinki University of Technology, Institute of Intelligent Power Electronics, Finland;University of California, Berkeley, Division of Biostatistics, Berkeley, CA;Helsinki University of Technology, Institute of Intelligent Power Electronics, Finland
Venue:
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Year:
2006

Citing 5
Cited 0

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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 this data is assessed using a multiple hypothesis testing framework relying on a bootstrap resampling procedure. The proposed method offers a convenient, flexible, and reliable approach to comparing algorithms in a wide variety of applications.