Abstract
Theory and experiments are complementary ways to analyze optimization algorithms. Experiments can also live a life of their own and produce learning without need to follow or test a theory. Yet, in order to make conclusions based on experiments trustworthy, reliable, and objective a systematic methodology is needed. In the natural sciences, this methodology relies on the mathematical framework of statistics. This book collects the results of recent research that focused on the application of statistical principles to the specific task of analyzing optimization algorithms.
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Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (2010). Introduction. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds) Experimental Methods for the Analysis of Optimization Algorithms. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02538-9_1
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DOI: https://doi.org/10.1007/978-3-642-02538-9_1
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