A General Framework for Statistical Performance Comparison of Evolutionary Computation Algorithms

D. Shilane (USA), J. Martikainen (Finland), S. Dudoit (USA), and S.J. Ovaska (Finland)


Evolutionary computation, statistics, performance comparison, twofold sampling, bootstrap, multiple hypothesis testing.


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.

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