Abstract
It is obvious that, given a problem instance, some heuristic algorithms can perform vastly better than others; however, in most cases the existing literature provides little guidance for choosing the best heuristic algorithm. This paper describes how runtime performance predictors can be used to identify a good algorithm for a particular problem instance. The approach is demonstrated on two families of heuristic algorithms.
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P. Cheeseman, B. Kanefsky, and W. Taylor. Where the really hard problems are. In Proceedings of the 12 th IJCAI, pages 331–337, Sydney, Australia, 1991. Morgan Kaufmann.
F. Glover. Tabu search — part I. ORSA Journal on Computing, 1(3):190–206, 1989.
F. Glover. Tabu search — part II. ORSA Journal on Computing, 2:4–32, 1990.
M. Johnston and S. Minton. Analyzing a heuristic strategy for constraint-satisfaction and scheduling. In M. Zweben and M. Fox, editors, Intelligent Scheduling, pages 257–290. Morgan Kaufmann, 1994.
L. Kale. An almost perfect heuristic for the n nonattacking queens problem. Inf. Process. Lett, 34:173–178, 1990.
D. Knuth. Estimating the efficiency of backtrack programs. Mathematics of Computation, 29:121–136, 1975.
V. Kumar. Algorithms for constraint-satisfaction problems: a survey. AI Magazine, 13(1):32–44, 1992.
S. Minton. Integrating heuristics for constraint satisfaction problems: A case study. In Proceedings of the Eleventh National Conference on Artificial Intelligence, San Jose, CA, 1993. AAAI Press.
S. Minton, M. Johnston, A. Philips, and P. Laird. Minimizing conficts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58:161–205, 1992.
D. Mitchell, B. Selman, and H. Levesque. Hard and easy distributions of SAT problems. In Proceedings of AAAI-92, pages 459–465, San Jose, CA, 1991. AAAI Press.
N. Muscettola. HSTS: Integrating planning and scheduling. In M. Zweben and M. Fox, editors, Intelligent Scheduling, pages 169–212. Morgan Kaufmann, 1994.
B. Nadel. Tree search and arc consistency in constraint satisfaction algorithms. In L. Kanal and V. Kumar, editors, Search in Artificial Intelligence, pages 287–342. Springer-Verlag, 1988.
V. Rao and V. Kumar. Superlinear speedup in state-space search. In Conference on foundations of softwar technology and theoretical computer science, 1988.
D. Sabin and E. Freuder. Constradicting conventional wisdom in constraint satisfaction. In A. Borning, editor, Proceedings of the 1994 Workshop on Principles and Practice of Constraint Programming, Orcas Island, Washington, 1994. Springer-Verlag.
N. Sadeh. Look-ahead techniques for micro-opportunist job shop scheduling. Technical Report CMU-CS-91-102, School of Computer Science, Carnegie Mellon, 1991.
N. Sadeh. Micro-opportunistic scheduling: the micro-boss factory scheduler. In M. Zweben and M. Fox, editors, Intelligent Scheduling, pages 99–135. Morgan Kaufmann, 1994.
B. Selman, H. Levesque, and D. Mitchell. A new method for solving hard satisfiability problems. In Proceedings of AAAI-92, pages 440–446, San Jose, CA, 1992. AAAI Press.
D. Smith. Transformational approach to scheduling. Technical Report KES.U.92.2, Kestrel Institute, 1992.
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© 1996 Springer-Verlag Berlin Heidelberg
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Allen, J.A., Minton, S. (1996). Selecting the right heuristic algorithm: Runtime performance predictors. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_40
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DOI: https://doi.org/10.1007/3-540-61291-2_40
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