On Starting Population Selection for GSAT

Anna Gorbenko; Vladimir Popov

doi:10.4028/www.scientific.net/AMM.365-366.190

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 365-366On Starting Population Selection for GSAT

On Starting Population Selection for GSAT

Abstract:

GSAT is a well-known satisfiability search algorithm for conjunctive normal forms. GSAT uses some random functions. One of such functions is a function of starting population of truth assignments for the variables of conjunctive normal form. In this paper, we consider a method of artificial physics optimization for computing a function of starting population.

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Periodical:

Applied Mechanics and Materials (Volumes 365-366)

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190-193

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.365-366.190

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Online since:

August 2013

Authors:

Anna Gorbenko, Vladimir Popov

Keywords:

Artificial Physics Optimization, GSAT, Runge Kutta Neural Network, Satisfiability

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References

[1] A. Gorbenko, V. Popov, Programming for Modular Reconfigurable Robots, Programming and Computer Software 38 (2012) 13-23.

DOI: 10.1134/s0361768812010033

Google Scholar

[2] A. Gorbenko, Graph-Theoretic Models for the Module of Safe Planning for Control Systems of Mobile Robots, Advanced Materials Research 683 (2013) 737-740.

DOI: 10.4028/www.scientific.net/amr.683.737

Google Scholar

[3] A. Gorbenko, V. Popov, Self-Learning of Robots and the Model of Hamiltonian Path with Fixed Number of Color Repetitions for Systems of Scenarios Creation, Advanced Materials Research 683 (2013) 909-912.

DOI: 10.4028/www.scientific.net/amr.683.909

Google Scholar

[4] A. Gorbenko, V. Popov, The set of parameterized k-covers problem, Theor. Comput. Sci. 423 (2012) 19-24.

Google Scholar

[5] B. Selman, H. Levesque, D. Mitchell, A new method for solving hard satisfiability problems, in: Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI Press, 1992, pp.440-446.

Google Scholar

[6] A. Gorbenko, V. Popov, The Force Law Design of Artificial Physics Optimization for Starting Population Selection for GSAT, Adv. Studies Theor. Phys. 7 (2013) 131-134.

DOI: 10.12988/astp.2013.13011

Google Scholar

[7] L. Xie, J. Zeng, The Performance Analysis of Artificial Physics Optimization Algorithm Driven by Different Virtual Forces, ICIC Express Letters 4 (2010) 239-244.

Google Scholar

[8] Y. -J. Wang, C. -T. Lin, Runge Kutta neural network for identification of continuous systems, in: Proceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics, IEEE Press, 1998, pp.3277-3282.

DOI: 10.1109/icsmc.1998.726509

Google Scholar

[9] V. Popov, Computational complexity of problems related to DNA sequencing by hybridization, Dokl. Math. 72 (2005) 642-644.

Google Scholar

[10] V. Popov, Multiple genome rearrangement by swaps and by element duplications, Theor. Comput. Sci. 385 (2007) 115-126.

DOI: 10.1016/j.tcs.2007.05.029

Google Scholar

[11] V. Popov, The approximate period problem for DNA alphabet, Theor. Comput. Sci. 304 (2003) 443-447.

Google Scholar

[12] SATLIB: http: /people. cs. ubc. ca/~hoos/SATLIB/index-ubc. html.

Google Scholar

[13] F. Lardeux, F. Saubion, J. -K. Hao, GASAT: a genetic local search algorithm for the satisfiability problem, Evolutionary Computation, 14 (2006) 223-253.

DOI: 10.1162/evco.2006.14.2.223

Google Scholar