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

Felipe Campelo, Fernanda Takahashi

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)305-338
Number of pages34
JournalJournal of Heuristics
Volume25
Issue number2
Early online date4 Oct 2018
DOIs
Publication statusPublished - 15 Apr 2019

Bibliographical note

© Springer Nature B.V. 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10732-018-9396-7

Keywords

  • Accuracy of parameter estimation
  • Experimental comparison of algorithms
  • Iterative sampling
  • Sample size estimation
  • Statistical methods

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