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

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.