Abstract
Satisfiability testing (SAT) is a very active area of research today, with numerous real-world applications. We describe CLASS2.0, a genetic programming system for semi-automatically designing SAT local search heuristics. An empirical comparison shows that that the heuristics generated by our GP system outperform the state of the art human-designed local search algorithms, as well as previously proposed evolutionary approaches, with respect to both runtime as well as search efficiency (number of variable flips to solve a problem).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fukunaga, A.: Automated discovery of composite sat variable-selection heuristics. In: Proc. AAAI, pp. 641–648 (2002)
Fukunaga, A.: Efficient implementations of sat local search. To appear in Proceedings of SAT-2004 (May 2004)
Gottlieb, J., Marchiori, E., Rossi, C.: Evolutionary algorithms for the satisfiability problem. Evolutionary Computation 10(1), 35–50 (2002)
Hoos, H.: Stochastic local search - methods, models, applications. PhD thesis, TU Darmstadt (1998)
Hoos, H., Stutzle, T.: Local search algorithms for sat: An empirical evaluation. Journal of Automated Reasoning 24, 421–481 (2000)
Koza, J.: Genetic Programming: On the Programming of Computers By the Means of Natural Selection. MIT Press, Cambridge (1992)
McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proc. AAAI, pp. 459–465 (1997)
Mitchell, D., Selman, B., Levesque, H.: Hard and easy distributions of sat problems. In: Proc. AAAI, pp. 459–465 (1992)
Montana, D.: Strongly typed genetic programming. Technical report, Bolt, Beranek and Neuman (BBN) (1993)
Schuurmans, D., Southey, F.: Local search characteristics of incomplete sat procedures. Artificial Intelligence 132, 121–150 (2001)
Selman, B., Kautz, H.: Domain-independent extensions to gsat: Solving large structured satisfiability problems. In: Proc. Intl. Joint Conf. Artificial Intelligence, IJCAI (1993)
Selman, B., Kautz, H., Cohen, B.: Noise strategies for improving local search. In: Proc. AAAI (1994)
Selman, B., Levesque, H., Mitchell, D.: A new method for solving hard satisfiability problems. In: Proc. AAAI, pp. 440–446 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fukunaga, A.S. (2004). Evolving Local Search Heuristics for SAT Using Genetic Programming. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-24855-2_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22343-6
Online ISBN: 978-3-540-24855-2
eBook Packages: Springer Book Archive