Sample size estimation for power and accuracy in the experimental comparison of algorithms

Felipe Campelo () and Fernanda Takahashi ()
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Felipe Campelo: Universidade Federal de Minas Gerais
Fernanda Takahashi: Universidade Federal de Minas Gerais

Journal of Heuristics, 2019, vol. 25, issue 2, No 6, 305-338

Abstract: Abstract Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class. The proposed approach allows the experimenter to define desired levels of accuracy for estimates of mean performance differences on individual problem instances, as well as the desired statistical power for comparing mean performances over a problem class of interest. The method calculates the required number of problem instances, and runs the algorithms on each test instance so that the accuracy of the estimated differences in performance is controlled at the predefined level. Two examples illustrate the application of the proposed method, and its ability to achieve the desired statistical properties with a methodologically sound definition of the relevant sample sizes.

Keywords: Experimental comparison of algorithms; Statistical methods; Sample size estimation; Accuracy of parameter estimation; Iterative sampling (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10732-018-9396-7

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