Abstract
We propose a statistical methodology for comparing the performance of evolutionary algorithms that iteratively generate candidate optima over the course of many generations. Performance data are analyzed using multiple hypothesis testing to compare competing algorithms. Such comparisons may be drawn for general performance metrics of any iterative evolutionary algorithm with any data distribution. We also propose a data reduction technique to reduce computational costs.
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Shilane, D., Martikainen, J., Ovaska, S.J. (2009). Time-Dependent Performance Comparison of Evolutionary Algorithms. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_23
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DOI: https://doi.org/10.1007/978-3-642-04921-7_23
Publisher Name: Springer, Berlin, Heidelberg
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