×

zbMATH — the first resource for mathematics

Testing heuristics: We have it all wrong. (English) Zbl 0853.68155
Summary: The competitive nature of most algorithmic experimentation is a source of problems that are all too familiar to the research community. It is hard tomake fair comparisons between algorithms and to assemble realistic test problems. Competitive testing tells us which algorithm is faster but not why. Because it requires published code, it consumes time and energy that could be better spent doing more experiments. This article argues that a more scientific approach of controlled experimentation, similar to that used in other empirical sciences, avoids or alleviates these problems. We have confused research and development; competitive testing is suited only for the latter.

MSC:
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
PDF BibTeX Cite
Full Text: DOI
References:
[1] Aspvall, B. (1980). Recognizing disguised NR(1) instances of the satisfiability problem.Journal of Algorithms, 1, 97–103. · Zbl 0451.68037
[2] Böhm, H. (1992). Report on a SAT competition. Technical report no. 110, Universität Paderborn, Germany.
[3] Chandru, V., Coullard, C.R., Hammer, P.L., Montanez, M., and Sun, X. (1990). On renamable Horn and generalired Horn functions. InAnnals of Mathematics and Artificial Intelligence, Basel: Baltzer AG. · Zbl 0878.68106
[4] Chandru, V., and Hooker, J.N. (1992). Detecting extended Horn structure in propositional logic.Information Processing Letters, 42, 109–111. · Zbl 0780.68055
[5] Cheeseman, P., Kanefsky, B., and Taylor, W.M. (1991). Where the really hard problems are. InProceedings of the International Joint Conference on Artificial Intelligence, ICAI91, Sydney, Springer-Verlag (pp. 331–337). · Zbl 0747.68064
[6] Crawford, J.M., and Auton, L.D. (1993). Experimental results on the crossover point in satisfiability problems. InProceedings of the Eleventh National Conference on Artificial Intelligence, AAAI93, Washington, D.C., MIT Press (pp. 21–27).
[7] Gent, I.P., and Walsh, T. (1994). The SAT phase transition. In A.G. Cohn (Ed.),Proceedings of the Eleventh European Conference on Artificial Intelligence, ECAI94 (pp. 105–109).
[8] Harche, F., Hooker, J.N., and Thompson, G.L. (1994). A computational study of satisfiability algorithms for propositional logic.ORSA Journal on Computing 26, 423–435. · Zbl 0811.03004
[9] Hooker, J.N. (1994). Needed: An empirical science of algorithms.Operations Research, 42, 201–212. · Zbl 0805.90119
[10] Hooker, J.N., and Fedjki, C. (1990). Branch-and-cut solution of inference problems in propositional logic.Annals of Mathematics and Artificial Intelligence, 1, 123–139. · Zbl 0878.68065
[11] Hooker, J.N., and Vinay, V. (Forthcoming). Branching rules for satisfiability.Journal of Automated Reasoning. · Zbl 0838.68098
[12] Larrabee, T., and Tsujii, Y. (1993). Evidence for a satisfiability threshold for random 3cnf formulas. In H. Hirsch et al. (Eds.),Proceedings of the Spring Symposium on Artificial Intelligence and NP-Hard Problems (pp. 112–118). Stanford, CA.
[13] Lustig, I.J., Marsten, R.E., and Shanno, D.F. (1994). Interior point methods for linear programming: Computational state of the art.ORSA Journal on Computing, 6, pp. 1–14). · Zbl 0798.90100
[14] McGeoch, C.C. (Forthcoming). Toward an experimental method for algorithm simulations.ORSA Journal on Computing. · Zbl 0854.68038
[15] Mitchell, D., Selman, B., and Levesque, H. (1992). Hard and easy distributions of SAT problems. InProceedings, Tenth National Conference on Artificial Intelligence, AAAI92 (pp. 459–465). Cambridge, MA: MIT Press.
[16] Trick, J., and Johnson, D.S. (Eds.). (1995).Second DIMACS Challenge: Cliques, Coloring and Satisfiability. Series in Discrete Mathematics and Theoretical Computer Science. Providence, RI: American Mathematical Society.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.