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

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 proble...

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Veröffentlicht in:Journal of heuristics Jg. 25; H. 2; S. 305 - 338
Hauptverfasser: Campelo, Felipe, Takahashi, Fernanda
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2019
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Artificial Intelligence
Calculus of Variations and Optimal Control; Optimization
Estimates
Management Science
Mathematics
Mathematics and Statistics
Operations Research
Operations Research/Decision Theory
Optimization algorithms
Iterative sampling
Statistical methods
Sample size estimation
Accuracy of parameter estimation
Experimental comparison of algorithms
ISSN:1381-1231, 1572-9397
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Zusammenfassung: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.
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ISSN:1381-1231
1572-9397
DOI:10.1007/s10732-018-9396-7