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Needed: An Empirical Science of Algorithms

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  • J. N. Hooker

    (Carnegie Mellon University, Pittsburgh, Pennsylvania)

Abstract

Deductive algorithmic science has reached a high level of sophistication, but its worst-case and average-case results seldom tell us how well an algorithm is actually going to work in practice. I argue that an empirical science of algorithms is a viable alternative. I respond to misgivings about an empirical approach, including the prevalent notion that only a deductive treatment can be “theoretical” or sophisticated. NP-completeness theory, for instance, is interesting partly because it has significant, if unacknowledged, empirical content. An empirical approach requires not only rigorous experimental design and analysis, but also the invention of empirically-based explanatory theories. I give some examples of recent work that partially achieves this aim.

Suggested Citation

  • J. N. Hooker, 1994. "Needed: An Empirical Science of Algorithms," Operations Research, INFORMS, vol. 42(2), pages 201-212, April.
    • Handle: RePEc:inm:oropre:v:42:y:1994:i:2:p:201-212
    DOI: 10.1287/opre.42.2.201
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    Citations

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    Cited by:

    1. Pablo Moscato & Michael G. Norman, 1998. "On the Performance of Heuristics on Finite and Infinite Fractal Instances of the Euclidean Traveling Salesman Problem," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 121-132, May.
    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.
    3. Amini, Mohammad M. & Racer, Michael & Ghandforoush, Parviz, 1998. "Heuristic sensitivity analysis in a combinatoric environment: An exposition and case study," European Journal of Operational Research, Elsevier, vol. 108(3), pages 604-617, August.
    4. Nicholas G. Hall & Marc E. Posner, 2001. "Generating Experimental Data for Computational Testing with Machine Scheduling Applications," Operations Research, INFORMS, vol. 49(6), pages 854-865, December.
    5. John Nartey Kanamitie & John Nketsiah & Kennedy Asenso, 2023. "English Language Proficiency: A Predictor of Academic Performance in Biology," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 7(3), pages 358-367, March.
    6. Raymond R. Hill & Charles H. Reilly, 2000. "The Effects of Coefficient Correlation Structure in Two-Dimensional Knapsack Problems on Solution Procedure Performance," Management Science, INFORMS, vol. 46(2), pages 302-317, February.
    7. Pablo Moscato & Luke Mathieson & Mohammad Nazmul Haque, 2021. "Augmented intuition: a bridge between theory and practice," Journal of Heuristics, Springer, vol. 27(4), pages 497-547, August.
    8. Reilly, Charles H. & Sapkota, Nabin, 2015. "A family of composite discrete bivariate distributions with uniform marginals for simulating realistic and challenging optimization-problem instances," European Journal of Operational Research, Elsevier, vol. 241(3), pages 642-652.
    9. Fink, Andreas & Vo[ss], Stefan, 2003. "Solving the continuous flow-shop scheduling problem by metaheuristics," European Journal of Operational Research, Elsevier, vol. 151(2), pages 400-414, December.
    10. Haipeng Guo & William Hsu, 2007. "A machine learning approach to algorithm selection for $\mathcal{NP}$ -hard optimization problems: a case study on the MPE problem," Annals of Operations Research, Springer, vol. 156(1), pages 61-82, December.
    11. Charles H. Reilly, 2009. "Synthetic Optimization Problem Generation: Show Us the Correlations!," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 458-467, August.
    12. Mastrolilli, Monaldo & Bianchi, Leonora, 2005. "Core instances for testing: A case study," European Journal of Operational Research, Elsevier, vol. 166(1), pages 51-62, October.

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