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SYNTHESE
2008

How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing

8 years 3 months ago
How experimental algorithmics can benefit from Mayo's extensions to Neyman-Pearson theory of testing
Although theoretical results for several algorithms in many application domains were presented during the last decades, not all algorithms can be analyzed fully theoretically. Experimentation is necessary. The analysis of algorithms should follow the same principles and standards of other empirical sciences. This article focuses on stochastic search algorithms, such as evolutionary algorithms or particle swarm optimization. Stochastic search algorithms tackle hard real-world optimization problems, e.g., problems from chemical engineering, airfoil optimization, or bioinformatics, where classical methods from mathematical optimization fail. Nowadays statistical tools that are able to cope with problems like small sample sizes, nonnormal distributions, noisy results, etc. are developed for the analysis of algorithms. Although there are adequate tools to discuss the statistical significance of experimental data, statistical significance is not scientifically meaningful per se. It is neces...
Thomas Bartz-Beielstein
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where SYNTHESE
Authors Thomas Bartz-Beielstein
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