Skip to main content
SpringerLink
Log in

Evolving Local Search Heuristics for SAT Using Genetic Programming

  • Conference paper
Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

Included in the following conference series:

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 to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Fukunaga, A.: Automated discovery of composite sat variable-selection heuristics. In: Proc. AAAI, pp. 641–648 (2002)

    Google Scholar 

  2. Fukunaga, A.: Efficient implementations of sat local search. To appear in Proceedings of SAT-2004 (May 2004)

    Google Scholar 

  3. Gottlieb, J., Marchiori, E., Rossi, C.: Evolutionary algorithms for the satisfiability problem. Evolutionary Computation 10(1), 35–50 (2002)

    Article  Google Scholar 

  4. Hoos, H.: Stochastic local search - methods, models, applications. PhD thesis, TU Darmstadt (1998)

    Google Scholar 

  5. Hoos, H., Stutzle, T.: Local search algorithms for sat: An empirical evaluation. Journal of Automated Reasoning 24, 421–481 (2000)

    Article  MATH  Google Scholar 

  6. Koza, J.: Genetic Programming: On the Programming of Computers By the Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  7. McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proc. AAAI, pp. 459–465 (1997)

    Google Scholar 

  8. Mitchell, D., Selman, B., Levesque, H.: Hard and easy distributions of sat problems. In: Proc. AAAI, pp. 459–465 (1992)

    Google Scholar 

  9. Montana, D.: Strongly typed genetic programming. Technical report, Bolt, Beranek and Neuman (BBN) (1993)

    Google Scholar 

  10. Schuurmans, D., Southey, F.: Local search characteristics of incomplete sat procedures. Artificial Intelligence 132, 121–150 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  11. Selman, B., Kautz, H.: Domain-independent extensions to gsat: Solving large structured satisfiability problems. In: Proc. Intl. Joint Conf. Artificial Intelligence, IJCAI (1993)

    Google Scholar 

  12. Selman, B., Kautz, H., Cohen, B.: Noise strategies for improving local search. In: Proc. AAAI (1994)

    Google Scholar 

  13. Selman, B., Levesque, H., Mitchell, D.: A new method for solving hard satisfiability problems. In: Proc. AAAI, pp. 440–446 (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, CA, 90024, USA

    Alex S. Fukunaga

Authors
  1. Alex S. Fukunaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

Rights and permissions

Reprints 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

Publish with us

Policies and ethics

Search

Navigation