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

Author

Listed:
  • Felipe Campelo

    (Universidade Federal de Minas Gerais)

  • Fernanda Takahashi

    (Universidade Federal de Minas Gerais)

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.

Suggested Citation

  • Felipe Campelo & Fernanda Takahashi, 2019. "Sample size estimation for power and accuracy in the experimental comparison of algorithms," Journal of Heuristics, Springer, vol. 25(2), pages 305-338, April.
    • Handle: RePEc:spr:joheur:v:25:y:2019:i:2:d:10.1007_s10732-018-9396-7
    DOI: 10.1007/s10732-018-9396-7
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    References listed on IDEAS

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    2. Marie Coffin & Matthew J. Saltzman, 2000. "Statistical Analysis of Computational Tests of Algorithms and Heuristics," INFORMS Journal on Computing, INFORMS, vol. 12(1), pages 24-44, February.
    3. Vallada, Eva & Ruiz, Rubén, 2011. "A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times," European Journal of Operational Research, Elsevier, vol. 211(3), pages 612-622, June.
    4. Catherine C. McGeoch, 1996. "Feature Article---Toward an Experimental Method for Algorithm Simulation," INFORMS Journal on Computing, INFORMS, vol. 8(1), pages 1-15, February.
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    Cited by:

    1. Singh, Bikramjit & Singh, Amarinder, 2023. "Hybrid particle swarm optimization for pure integer linear solid transportation problem," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 243-266.
    2. Felipe Campelo & Elizabeth F. Wanner, 2020. "Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances," Journal of Heuristics, Springer, vol. 26(6), pages 851-883, December.

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