Optimizing the Initialization of Dynamic Decision Heuristics in DPLL SAT Solvers Using Genetic Programming-Reference-Cited by-同舟云学术

Optimizing the Initialization of Dynamic Decision Heuristics in DPLL SAT Solvers Using Genetic Programming

Published:2006 Issue: Volume: Page:331-340
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
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Author:

Kibria Raihan H.,Li You

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11729976_30.pdf

Reference8 articles.

1. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts (1994)

2. Davis, M., Logemann, G., Loveland, D.: A machine program for theorem proving. Communications of the ACM, 5 (1962)

3. Moskewicz, M.W., Madigan, C.F., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an Efficient SAT Solver. In: Proc. of the 38th Design Automation Conference (2001)

4. Een, N., Sörensson, N.: An Extensible SAT-solver. SAT (2003)

5. Hollick, M., Kuhlmann, H.: Genetic Programming in C/C++. CSE99/CIS899 Final Report (May 1995), http://www.cis.upenn.edu/~hollick/genetic/paper2.html

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