ijece-140502291914050229192544CMV Verlagkhoffmann@cmv-verlag.comCMV VerlagNashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iranijece168237459212013112K.EtminaniM.NaghibzadehM.EmadiA. R.Razavi921201311210.66224/ijece.28053.11.2.1http://ijece.org/fa/Article/28053http://ijece.org/fa/Article/Download/28053http://ijece.org/fa/Article/Download/28053http://ijece.org/fa/Article/Download/28053http://ijece.org/fa/Article/Download/28053http://ijece.org/fa/Article/Download/28053http://ijece.org/fa/Article/Download/28053http://ijece.org/fa/Article/Download/28053[1] N. Friedman, M. Linial, I. Nachman, and D. Peer, "Using Bayesian networks to analyze expression data," Computational Biology, vol. 7, no. 3-4, pp. 601-620, 2000. [2] J. Uebersax, Breast Cancer Risk Modeling: An Application of Bayes Networks, Technical Report, Ravenpack International, Spain, 2004. [3] L. M. de Campos, J. M. Fernandez - Luna, and J. F. Huete, "Bayesian networks and information retrieval: an introduction to the special issue," Information Processing and Management, vol. 40, no. 5, pp. 727-733, Sep. 2004. [4] F. J. Diez, J. Mira, E. Iturralde, S. Zubillaga, and A. Diaval, "A Bayesian expert system for echocardiography," Artificial Intelligence in Medicine, vol. 10, no. 1, pp. 59-73, May 1997. [5] D. M. Chickering, "Learning bayesian network is NP - complete," Learning from Data: Artificial Intelligence and Statistics V, pp. 121-130, 1996. [6] D. M. Chickering, C. Meek, and D. Heckerman, "Large-sample learning of Bayesian networks is NP-Hard," in Proc. 19th Conf. on Uncertainty in Artificial Intelligence, UAI'03, pp. 124-133, 2003. [7] Z. Kebaili and A. Aussem, "A novel hybrid Bayesian network structure learning algorithm based on correlated itemset mining techniques," Int. J. of Computational Intelligence Research, vol. 5, no. 1, pp. 16-21, 2009. [8] I. Tsamardinos, L. E. Brown, and C. F. Aliferis, "The max - min hill - climbing Bayesian network structure learning algorithm," Machine Learning, vol. 65, no. 1, pp. 31-78, 2006. [9] D. Rubin, "Inference and missing data," Biometrika, vol. 63, no. 3, pp. 581-592, 1976. [10] R. Little and D. Rubin, Statistical Analysis with Missing Data, Wiley - Interscience, 2002. [11] G. Elidan, I. Nachman, and N. Friedman, "Ideal parent structure learning for continuous variable Bayesian networks," J. of Machine Learning Research, vol. 8, pp. 1799-1833, 2007. [12] W. Buntine, "Theory refinement on Bayesian networks," in Proc. 7th Conf. on Uncertainty in Artificial Intelligence, UAI'91, pp. 52-60, 1991. [13] T. Silander and P. Myllymaki, "A simple approach for finding the globally optimal Bayesian network structure," in Proc. 22nd Conf. on Uncertainty in Artificial Intelligence, UAI'06, pp. 445-452, 2006. [14] A. P. Singh and A. W. Moore, Finding Optimal Bayesian Networks by Dynamic Programming, Technical Report, Carnegie Mellon University CALD-05-106, 2005. [15] M. Koivisto and K. Sood, "Exact bayesian structure discovery in bayesian networks," J. of Machine Learning Research, vol. 5, pp. 549-573, 2004. [16] J. Suzuki, "Learning Bayesian belief networks based on the minimum description length principle: an efficient algorithm using the branch and bound technique," IEICE Trans. Inf. & Syst., vol. E82-D, no. 2, pp. 356-367, Feb. 1999. [17] C. P. de Campos, Z. Zeng, and Q. Ji, "Structure learning of Bayesian networks using constraints," in Proc. 26th Int. Conf. on Machine Learning, ICML'09, 2009. [18] K. Etminani, M. Naghibzadeh, and A. R. Razavi, "Globally optimal structure learning of bayesian networks from data," Lecture Notes in Computer Science, vol. 6352, pp. 101-106, 2010. [19] D. M. Chickering and D. Heckerman, "Efficient approximations for the marginal likelihood of bayesian networks with hidden variables," Machine Learning, vol. 29, no. 2-3, pp. 181-212, 1997. [20] M. Ramoni and P. Sebastiani, "Learning Bayesian networks from incomplete databases," in Proc. of the Conf. on Uncertainty in AI, pp. 401-408, 1997. [21] N. Friedman, "Learning belief network in the presence of missing values and hidden variables," in Proc.14th Int. Conf. on Machine Learning, pp. 125-133, 1997. [22] N. Friedman, "The bayesian structural EM algorithm," in Proc. 14th Conf. on Uncertainty in Artificial Intelligence, 1998. [23] A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. of the Royal Statistical Society B, vol. 39, no. 1, pp. 1-39, 1977. [24] P. Leray and O. Franois, "Bayesian network structural learning and incomplete data," in Proc. Int. and Interdisciplinary Conf. on Adaptive Knowledge Representation and Reasoning, 2005. [25] N. Friedman and M. Goldszmidt, "Discretizing continuous attributes while learning bayesian networks," in Proc. 13th Int. Conf. on Machine Learning, ICML'96, pp. 157-165, 1996. [26] G. Cooper and E. Herskovits, "A bayesian method for the induction of probabilistic networks from data," Machine Learning, vol. 9, no. 4, pp. 309-347, 1992. [27] D. Heckerman, D. Geiger, and D. M. Chickering, "Learning bayesian networks: the combination of knowledge and statistical data," Machine Learning, vol. 20, no. 3, pp. 197-243, 1995. [28] G. Schwartz, "Estimating the dimensions of a model," Annals of Statistics, vol. 6, no. 2, pp. 461-464, 1978. [29] J. Rissanen, "Stochastic complexity (with discussion)," J. of the Royal Statistical Society, vol. 49, no. 3, pp. 223-239, 1987. [30] H. Akaike, "A new look at the statistical model identification," IEEE Trans. on Automatic Control, vol. 19, no. 6, pp. 716-723, 1974. [31] S. L. Lauritzen and D. J. Spiegelhalter, "Local computations with probabilitieson graphical structures and their application to expert systems," J. of Royal Statistics Society, vol. 50, no. 2, pp. 157-224, 1988. [32] R. E. Neapolitan, Probabilistic Reasoning in Expert Systems: Theory and Algorithms, John Wiley & Sons, Inc., pp. 179-180, 1990.S. M.Sharafi9212013132210.66224/ijece.28054.11.2.13http://ijece.org/fa/Article/28054http://ijece.org/fa/Article/Download/28054http://ijece.org/fa/Article/Download/28054http://ijece.org/fa/Article/Download/28054http://ijece.org/fa/Article/Download/28054http://ijece.org/fa/Article/Download/28054http://ijece.org/fa/Article/Download/28054http://ijece.org/fa/Article/Download/28054[1] L. Bass, P. Clements, and R. Kazman, Software Architecture in Practice, 2nd Edition, Addison-Wesley, 2003. [2] S. T. Albin, The Art of Software Architecture: Design Methods and Techniques, John Wiley & Sons, 2003. [3] P. Kruchten, The Rational Unified Process: An Introduction, 2nd Edition, Addison-Wesley, 2000. [4] R. Kazman, M. Klein, and P. Clements, ATAM: Method for Architecture Evaluation, Technical Report, CMU/SEI-2000-TR-004, ESC-TR-2000-004, 2000. [5] P. Clements, R. Kazman, and M. Klein, Evaluating Software Architectures: Methods and Case Studies, Addison-Wesley, 2002. [6] "CBAM: Cost Benefit Analysi Method", http://www.sei.cmu.edu/ata/products_services/cbam. [7] J. J. Li and J. R. Horgan, "Applying formal description techniques to software architectural design," J. of Computer Communications, vol. 23, no. 12, pp. 1169-1178, Jul. 2000. [8] X. He, H. Yu, T. Shi, J. Ding, and Y. Deng, "Formally analyzing software architectural specifications using SAM," The J. of Systems and Software, vol. 71, no. 12, pp. 11-29, 2004. [9] J. Xu, "Evaluating and balancing reliability and performance properties of software architecture using formal modeling techniques," in Proc. 30th Annual IEEE/NASA Software Engineering Workshop, pp. 212-222, Apr. 2006. [10] J. J. P. Tsai and K. Xu, "A comparative study of formal verification techniques for software architecture specifications," Annals of Software Engineering, vol. 10, no. 1-4, pp. 207-223, 2000. [11] L. Dai, Formal Design Analysis Framework: An Aspect-Oriented Architectural Framework, Ph.D. Dissertation, the University of Texas at Dallas, 2005. [12] K. Cooper, L. Dai, and Y. Deng, "Performance modeling and analysis of software architectures: an aspect-oriented UML based approach," Science of Computer Programming, vol. 57, no. 1, pp. 89-108, Jul. 2005. [13] L. Dai and K. Cooper, "Using FDAF to bridge the gap between enterprise and software architectures for security," Science of Computer Programming, vol. 66, no. 1, pp. 87-102, Apr. 2007. [14] T. J. Dolan, Architecture Assessment of Information-System Families, Ph.D. Thesis, Department of Technology Management, Eindhoven-University of Technology, Feb. 2002. [15] B. Tekinerdogan, H. Sozer, and M. Aksit, "Software architecture reliability analysis using failure scenarios," J. of Systems and Software, vol. 81, no. 4, pp. 558-575, Apr. 2008. [16] J. B. Dugan, "Software system analysis using fault trees," in Lyu, M. R. (Ed.), Handbook of Software Reliability Engineering, McGraw-Hill, New York, pp. 615-659, Chapter 15, 1996. [17] N. E. Fenton and M. Neil, "A critique of software defect prediction models," IEEE Trans. on Software Engineering, vol. 25, no. 5, pp. 675-689, Sep. 1999. [18] IEEE Std. 1044-2009, IEEE Standard Classification for Software Anomalies, pp. C1-15, 2010. [19] J. Yen and R. Langari, Fuzzy Logic: Intelligence, Control, and Information, Prentice-Hall, Upper Saddle River, NJ, 1999.M. PakdelM. H.Shakoor9212013233310.66224/ijece.28055.11.2.23http://ijece.org/fa/Article/28055http://ijece.org/fa/Article/Download/28055http://ijece.org/fa/Article/Download/28055http://ijece.org/fa/Article/Download/28055http://ijece.org/fa/Article/Download/28055http://ijece.org/fa/Article/Download/28055http://ijece.org/fa/Article/Download/28055http://ijece.org/fa/Article/Download/28055[1] M. Petrou and P. G. Sevilla, Image Processing Dealing with Texture, Ch. 2, John Wiely and Sons Ltd, pp. 1-6, 2006. [2] M. Tuceryan and A. K. Jain, "Texture Analysis," The Handbook of Pattern Recognition and Computer Vision, Ch. 2, pp. 207-248, World Scientific Publishing Co., 1998. [3] R. M. Haralick, K. Shanmugam, and I. Dinestein, "Textural features for image classification," IEEE Trans. on System, Man and Cybernetic, vol. 3, no. 6, pp. 610-621, Nov. 1979. [4] D. Popescu, R. Dobrescu, and M. Nicolae, "Texture classification and defect detection by statistical features," Int. J. of Circuit, System, and Signal Processing, vol. 1, no. 1, pp. 79-85, 2007. [5] G. R. Cross and A. K. Jain, "Markov random field texture models," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 5, no. 1, pp. 25-39, Jan. 1983. [6] M. Unser, "Texture classification and segmentation using wavelet frames," IEEE Trans. on Image Processing, vol. 4, no. 11, pp. 1549-1560, Nov. 1995. [7] F. Bianconi and A. Fernandez, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, vol. 40, no. 12, pp. 3325-3335, 2007. [8] M. Pakdel and F. Tajeripour, "Texture classification using optimal Gabor filters," in Proc. Int. eConf. on Computer and Knowledge Engineering, pp. 208-213, 13-14 Oct. 2011. [9] X. Chen, X. Zeng, and D. van Alphen, "Multi-class feature selection for texture classification," Pattern Recognition Letters, vol. 27, no. 14, pp. 1685-1691, 2006. [10] M. Pietikainen, T. Ojala, and Z. Xu, "Rotation-invariant texture classification using feature distributions," Pattern Recognition, vol. 33, pp. 43-52, 2000. [11] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002. [12] S. Arivazhagan and L. Ganesan, "Texture classification using wavelet transform," Pattern Recognition Letters, vol. 24, no. 9-10, pp. 1513-1521, 1 Jun. 2003. [13] B. V. Ramana Reddy, M. Radhika Mani, and K. V. Subbaiah, "Texture classification method using wavelet transform based on Gaussian Markov random field," International J. of Signal and Image Processing, vol. 1, no. 1, pp. 35-39, 2010. [14] V. Vijaya Kumar, U. S. N. Raju, M. Radhika Mani, and A. L. Narasimha Rao, "Wavelet based texture segmentation methods based on combinatorial of morphological and statistical operations," International J. of Computer Science and Network Security, vol. 8, no. 8, pp. 176-181, Aug. 2008. [15] J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. on System, Man and Cybernetic, vol. 6, no. 4, pp. 267-285, 1976. [16] T. Randen and J. H. Husoy, "Filtering for texture classification: a comparative study," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999. [17] T. Maenpaa, M. Pietikainen, and T. Ojala, "Texture classification by multi-predicate local binary pattern operators," in Proc. 15th Int. Conf. on Pattern Recognition, 3:951 - 954., vol. 3, pp. 939-942, Barcelona, Spain, 2000. [18] P. P. Koltsov, "Comparative study of texture detection and classification algorithms," Computational Mathematics and Mathematical Physics, vol. 51, no. 8, pp. 1460-1466, Aug. 2011. [19] F. Tajeripour, E. Kabir, and A. Sheikhi, "Fabric defect detection using Modified Local Binary Patterns," EURASIP J. on Advances in Signal Processing, vol. 8, pp. 1-12, 2008. [20] F. Bianconi and A. Fernandez, "On the occurrence probability of local binary patterns: a theoretical study," J. of Mathematical Imaging and Vision Manuscript, vol. 40, no. 3, pp. 259-268, 2011. [21] ف. تاجری‌پور، ا. ا. کبیر و ع. شیخی، "آشکارسازی عیوب بافتی با استفاده از شکل بهبودیافته الگوی باینری محلی،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، جلد 5، شماره 3، صص. 128-119، پاییز 1386. [22] T. Ojala, et al., "Outex - a new framework for empirical evaluation of texture analysis algorithms," in Proc. of 16th Int. Conf. of Pattern Recognition, 2002. [23] P. Brodatz, Textures: A Photographic Album for Artist and Designer, Dover, New York, 1966. [24] Z. Guo, L. Zhang, and D. Zheng, "Rotation invariant texture classification using LBP variance (LBPV) using global matching," Pattern Recognition, vol. 43, no. 3, pp. 706-719, Mar. 2010.F.AnooshaB.Tork LadaniM. A.Nematbakhsh9212013344210.66224/ijece.28056.11.2.34http://ijece.org/fa/Article/28056http://ijece.org/fa/Article/Download/28056http://ijece.org/fa/Article/Download/28056http://ijece.org/fa/Article/Download/28056http://ijece.org/fa/Article/Download/28056http://ijece.org/fa/Article/Download/28056http://ijece.org/fa/Article/Download/28056http://ijece.org/fa/Article/Download/28056[1] T. Berners - Lee, J. Hendler, and O. Lassila, "The semantic web," Scientific American, vol. 279, no. 5, pp. 28-37, May 2001. [2] D. Fensel, J. Hendler, H. Lieberman, and W. Wahlster, "Ontologies come of age," in Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential, MIT Press, 2003. [3] T. Lukasiewicz and U. Straccia, "Managing uncertainty and vagueness in description logics for the semantic web," J. of Web Semantics, vol. 6, no. 4, pp. 291-308, Nov. 2008. [4] B. Liu, J. Li, and Y. Zhao, "A query-specific reasoning method for inconsistent and uncertain ontology," in Proc. of the Int. MultiConf. of Engineers and Computer Scientists, IMECS, vol. 1, pp. 53-58, Hong Kong, Mar. 2011. [5] J. Zhao, "Advances in semantic computing," in Advances in Semantic Computing, pp. 1-22, 2010. Available http://www.tmrfindia.org/eseries/ebookV2.html [6] F. Martin - Recuerda and D. Robertson, "Discovery and uncertainty in semantic web services," in Proc. of Uncertainty Reasoning for the Semantic Web, URSW, pp. 188-123, 2005. [7] G. Stamou, J. V. Ossenbruggen, J. Z. Pan, and G. Schreiber, "Multimedia annotations on the semantic web," IEEE Multimedia, vol. 13, no. 1, pp. 86-90, 2006. [8] P. Haase and J. Volker, "Ontology learning and reasoning-dealing with uncertainty and inconsistency," in Proc. of the ISWC Workshop on Uncertainty Reasoning for the Semantic Web, URSW, pp. 45-55, Galway, Ireland, 2005. [9] A. Borgida and L. Serafini, "Distributed description logics: assimilating information from peer sources," J. of Data Semantics, vol. 1, pp. 153-184, 2003. [10] A. Tamilin, Distributed Ontological Reasoning: Theory, Algorithms, and Applications, Ph.D. Dissertation, University of Trento, 2007. [11] E. Sirin, B. Parsia, B. Grau, A. Kalyanpur, and Y. Katz, "Pellet: a practical OWL-DL reasoner," J. of Web Semantics, vol. 5, no. 2, pp. 51-53, 2007. [12] V. Haarslev and R. Moller, "Racer: a core inference engine for the semantic web ontology language (OWL)," in Proc. of the 2nd Int. Workshop on Evaluation of Ontology - Based Tools, pp. 27-36, 2003. [13] D. Tsarkov and I. Horrocks, "FaCT++ description logic reasoner," in Proc. of International Joint Conf. on All Aspects of Automated Reasoning (IJCAR), vol. 4130, pp. 292-297, 2006. [14] B. Motik and R. Studer, "KAON2 - a scalable reasoning tool for the semantic web," in Proc. of 2nd European Semantic Web Conf., ESWC'05, Greece, 2005. [15] A. Kiryakov, D. Ognyanov, and D. Manov, "OWLIM-a pragmatic semantic repository for OWL," in Proc. WISE 2005 Workshops LNCS, vol. 3807, pp. 182-192, Springer, Heidelberg, 2005. [16] J. Zhou, L. Ma, Q. Liu, L. Zhang, Y. Yu, and Y. Pan, "Minerva: a scalable OWL ontology storage and inference system," in Proc. Asian Semantic Web Conf., LNCS, vol. 4185, pp. 429-443, Springer, Heidelberg, 2006. [17] S. Bechhofer, I. Horrocks, and D. Turi, "The OWL instance store: system description," in Proc. CADE Conf., LNCS, vol. 3632, pp. 177-181, Springer, Heidelberg, 2005. [18] Q. Fang, Y. Zhao, G. Yang, and W. Zheng, "Scalable distributed ontology reasoning using DHT - based partitioning," in Proc. Asian Semantic Web Conf., ASWC, LNCS, vol. 5367, pp. 91-105, Springer, Heidelberg, 2008. [19] O. Kutz, C. Luts, F. Wolter, and M. Zakharyaschev, "E-connections of description logics," in Proc. of the 2003 Int. Workshop on Description Logics, pp. 178-187, 2003. [20] J. Bao, D. Caragea, and V. Honavar, "Modular ontologies - a formal investigation of semantics and expressivity," in Proc. of 1st Asian Semantic Web Conf., ASWC, LNCS, vol. 4185, pp. 615-631, Springer, Heidelberg, 2006. [21] F. Baader and U. Sattler, "Tableau algorithms for description logics," in Proc. of TABLEAUX, vol. 1847, pp. 1-18, 2000. [22] E. Oren, S. Kotoulas, G. Anadiotis, R. Siebes, A. T. Teije, and F. V. Harmelen, "MARVIN: distributed reasoning over large-scale semantic web data," J. of Web Semantics, vol. 7, no. 4, pp. 305-316, 2009. [23] A. Schlicht and H. Stuckenschmidt, "Towards distributed ontology reasoning for the web," in International Conf. on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 536-539, Dec. 2008. [24] T. Lukasiewicz, Probabilistic Description Logics for the Semantic Web, Technical Report, Knowledge - Based Systems Group, Vienna, 2007. [25] U. Straccia, "Towards a fuzzy description logic for the semantic web," in Proc. of 2nd of European Semantic Web Conf., ESWC, 2005. [26] G. Stoilos, G. Stamou, V. Tzouvaras, J. Pan, and I. Horrocks, "Fuzzy OWL: uncertainty and the semantic web," in Proc. of the Int. Workshop of OWL: Experiences and Directions, 2005. [27] J. Heinsohn, "Probabilistic description logics," in Proc. of UAI, pp. 311-318, 1994. [28] T. Lukasiewicz, "Fuzzy description logic programs under the answer set semantics for the semantic web," in Proc. of 2nd Int. Conf. on Rules and Rule Markup Languages for the Semantic Web, RuleML, pp. 89-96, 2006. [29] J. Pearl, Probabilistic Reasoning in Intelligent Systems Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988. [30] P. C. G. Costa and K. B. Laskey, "Pr-owl a framework for probabilistic ontologies," in Proc. FOIS 2006, pp. 237-249, 2006. [31] Z. Ding, Y. Peng, and R. Pan, "BayesOWL: uncertainty modeling in semantic web ontologies," Soft Computing in Ontologies and Semantic Web, vol. 204, pp. 3-29, 2006. [32] J. Durkin, Expert Systems: Design and Development, Macmillan, pp. 332-362, 1994. [33] E. Mokhtari, A Context-Aware Reputation-Based Trust Model for Multi-Agent Systems, M.S. Thesis, Dept. Computer Engineering, University of Isfahan, Isfahan, Iran, 2009.M. R.Mollakhalili MeybodiM. R.Meybodi9212013435110.66224/ijece.28057.11.2.43http://ijece.org/fa/Article/28057http://ijece.org/fa/Article/Download/28057http://ijece.org/fa/Article/Download/28057http://ijece.org/fa/Article/Download/28057http://ijece.org/fa/Article/Download/28057http://ijece.org/fa/Article/Download/28057http://ijece.org/fa/Article/Download/28057http://ijece.org/fa/Article/Download/28057[1] H. Beigy, M. R. Meybodi, and M. B. Menhaj, "Adaptation of learning rate in back propagation algorithm using fixed structure learning automata," in Proc. 6th Iranian Conf. on Electrical Engineering, pp. 117-123, Tehran, Iran, 1998. [2] H. Shah-Hosseini and R. Safabaksh, "Automatic adjustment of learning rates of the self-organizing feature map," Scientia Iranica, vol. 8, no. 4, pp. 277-286, Oct. 2001. [3] C. Chinrungrueng and C. H. Sequin, "Optimal adaptive k - means algorithm with dynamic adjustment of learnig rate," IEEE Trans. on Neural Networks, vol. 6, no. 1, pp. 157-169, Jan. 1995. [4] J. Akbari Torkestani and M. R. Meybodi, "Learning automata - based algorithms for solving stochastic minimum spanning tree problem," Applied Soft Computing, vol. 11, no. 6, pp. 4064-4077, Sep. 2011. [5] J. Akbari Torkestani and M. R. Meybodi, "Learning automata - based algorithms for finding minimum weakly connected dominating set in stochastic graphs," International J. of Uncertainty, Fuzziness, and Knowledge - Based Systems, vol. 18, no. 6, pp. 721-758, Dec. 2010. [6] H. Beigy and M. R. Meybodi, "Utilizing distributed learning automata to solve stochastic shortest path problems," International J. of Uncertainty, vol. 14, no. 5, pp. 591-615, Oct. 2006. [7] M. R. Mollakhalili Meybodi and M. R. Meybodi, "A new distributed learning automata based algorithm for solving stochastic shortest path," in Proc. 6th Conf. on Intelligent Systems, Kerman, Iran, 26-27 Nov. 2004. [8] M. R. Meybodi and H. Beigy, "Solving stochastic shortest path problem using distributed learning automata," in Proc. 6th Annual CSI Computer Conf., CSICC 2001, pp. 70-86, Isfehan, Iran, Feb. 2001. [9] H. Beigy Intelligent Channel Assignment in Cellular Networks: A Learning Automata Approach, Ph.D. Thesis, Amirkabir University of Technology, 2004. [10] M. L. Thathachar and P. S. Sastry, "Varieties of learning automata: an overview," IEEE Trans. on Systems, Man, and Cybernetics, Part B, Cybernetics, vol. 32, no. 6, pp. 711-22, Jan. 2002. [11] K. S. Narenda and M. A. L. Thatacher, Learning Automata, Prentice - Hall, 1989. [12] M. L. Tsetlin, "On the behaviour of finite automata in random media," Automata., Telemekh., vol. 22, pp. 1345-1354, Oct. 1961. [13] B. J. Oomen and H. Masum, "Switching models for nonstationary random environments," IEEE Trans. on Systems, Man, and Cybernetics., vol. 25, no. 9, pp. 1334-1347, Sep. 1995. [14] K. S. Narendra and M. A. L. Thathachar, "On the behavior of a learning automaton in a changing environment with application to telephone traffic routing," Systems, Man, and Cybernetics, IEEE Trans. on, vol. 10, no. 5, pp. 262-269, May 1980. [15] S. M. Ross, Introduction to Probability and Statistics dor Engineers and Scientists, 3rd. Elsevier Academic Press, 2004. [16] A. Papoulis, Probability, Random Variables, and Stochastic Processes, 3rd Ed. New York, USA: McGrawHill, 1991.M. H.Mozafari MarefSeyed-HamidZahiri9212013525710.66224/ijece.28058.11.2.52http://ijece.org/fa/Article/28058http://ijece.org/fa/Article/Download/28058http://ijece.org/fa/Article/Download/28058http://ijece.org/fa/Article/Download/28058http://ijece.org/fa/Article/Download/28058http://ijece.org/fa/Article/Download/28058http://ijece.org/fa/Article/Download/28058http://ijece.org/fa/Article/Download/28058[1] R. A. Formato, Central Force Optimization: A New Nature Inspired Computational Framework for Multidimensional Search and Optimization, Studies in Computational Intelligence 129, pp. 221-238, 2008. [2] R. A. Formato, "Central force optimization: a new metaheuristic with applications in applied electromagnetic," Progress in Electromagnetics Research, vol. 77, no. 1, pp. 425-491, Sep. 2007. [3] G. M. Qubati, R. A. Formato, and N. I. Dib, "Antenna benchmark performance and array synthesis using central force optimization," Microwaves, Antennas, and Propagation, IET, vol. 4, no. 5, pp. 583-592, Oct. 2010. [4] I. E. Evangelou, D. G. Hadjimitsis, A. A. Lazakidou, and C. Clayton, "Data mining and knowledge discovery in complex image data using artificial neural networks," in Proc. Workshop on Complex Reasoning and Geographical Data, Aug. 2001. [5] T. Lillesand and R. Keifer, Remote Sensing and Image Interpretation, John Wiley & Sons, 1994. [6] J. A. Hartigan, Clustering Algorithms, John Wiley & Sons, 1975. [7] U. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," J. of the Pattern Recognition, vol. 33, no. 9, pp. 1455-1465, Dec. 2000. [8] L. Y. Tseng and S. B. Yang, "A genetic approach to the automatic clustering problem," J. of the Pattern Recognition, vol. 34, no. 3, pp. 415-424, May. 2001. [9] D. W. van der Merwe and A. P. Engelbrecht, "Data clustering using particle swarm optimization," in Proc. of the 2003 Congress on Evolutionary Computation, vol. 1, pp. 215-220, Sep. 2003. [10] D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE Trans. Pattern Anal. Machine Intell, vol. 12, no. 3, pp. 234-246, Sep. 1979. [11] S. Bandyopadhyay and U. Maulik, "Genetic clustering for automatic evolution of clusters and application to image classification," IEEE Pattern Recognition Con., vol. 1, no. 1, pp. 421-427, Oct. 2002. [12] V. Katari and S. C. Satapathy, "Hybridized improved genetic algorithm with variable length chromosome for image clustering," IJCSNS International J. of Computer Science and Network Security, vol. 26, no. 4, pp. 135-152, Aug. 2007. [13] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Reading, MA: Addison-Wesley, 1989.