Binary optimization: A relation between the depth of a local minimum and the probability of its detection

ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings

Volumn ICSO, Issue , 2007, Pages 5-10

  Kryzhanovsky, B V ^a   Kryzhanovsky, V M ^a   Mikaelian, A L ^a  

^a RUSSIAN ACADEMY OF SCIENCES   (Russian Federation)

Author keywords

Binary optimization; Local minimum; Neural networks

Indexed keywords

BASIN OF ATTRACTION; BINARY OPTIMIZATION; ENERGY MINIMA; GLOBAL MINIMA; HOPFIELD MODELS; INITIAL CONFIGURATION; LOCAL MINIMUM; LOCAL MINIMUMS; NEURON NETWORKS; OPTIMIZATION PROBLEMS; RANDOM SEARCHES; STANDARD METHOD;

NEURAL NETWORKS; PROBABILITY; ROBOTICS;

OPTIMIZATION;

EID: 67149130072     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Amit, D.J., Gutfreund, H., Sompolinsky, H., 1985. Spinglass models of neural networks. Physical Review A, v.32, pp.1007-1018.
2. Fu, Y., Anderson, P.W., 1986. Application of statistical mechanics to NP-complete problems in combinatorial optimization. Journal of Physics A. , v. 19, pp. 1605-1620.
3. Hartmann, A.K., Rieger, H., 2004. New Optimization Algorithms in Physics., Wiley-VCH, Berlin.
4. Hopfield, J.J. 1982. Neural Networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci.USA. v.79, pp.2554-2558.
5. Hopfield,J.J.,Tank,D.W.,1985. Neuralcomputationofdecisionsinoptimizationproblems.BiologicalCybernetics,v.52, 141-152.(Pubitemid15007505)
6. Huajin Tang; Tan, K.C.; Zhang Yi, 2004. A columnar competitive model for solving combinatorial optimization problems. IEEE Trans. Neural Networks v. 15, pp. 1568-1574.
7. Joya, G., Atencia, M., Sandoval, F., 2002. Hopfield Neural Networks for Optimization: Study of the Different Dynamics. Neurocomputing, v.43, pp. 219-237. (Pubitemid 34041235)
8. Kryzhanovsky, B., Magomedov, B., 2005. Application of domain neural network to optimization tasks. Proc. of ICANN'2005. Warsaw. LNCS 3697, Part II, pp.397-403.
9. Kryzhanovsky, B., Magomedov, B., Fonarev, A., 2006. On the Probability of Finding Local Minima in Optimization Problems. Proc. of International Joint Conf. on Neural Networks IJCNN-2006 Vancouver, pp.5882-5887.
10. Kwok, T., Smith, K.A., 2004. A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization. IEEE Trans. Neural Networks v. 15, pp.84 - 98.
11. Perez-Vincente, C.J., 1989. Finite capacity of sparcecoding model. Europhys. Lett., v.10, pp.627-631.
12. Poggio,T.,Girosi,F.,1990. Regularizationalgorithmsforlearningthatareequivalent-multilayernetworks. Science247,978-982.(Pubitemid20081666)
13. Salcedo-Sanz, S.; Santiago-Mozos, R.; Bousono-Calzon, C., 2004. A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems. IEEE Trans. Systems, Man and Cybernetics, v. 34,1108-1116
14. Smith, K.A. 1999. Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research. INFORMS Journal on Computing v.11 (1), pp. 15-34.
15. Wang, L.P., Li, S., Tian F.Y, Fu, X.J., 2004. A noisy chaotic neural network for solving combinatorial optimization problems: Stochastic chaotic simulated annealing. IEEE Trans. System, Man, Cybern, Part B - Cybernetics v.34, pp. 2119-2125.
16. Wang, L.P., Shi, H., 2006: A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks. IEEE Trans. Neural Networks, vol.17, pp.989 -1000.