Applied and Computational Mathematics

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A Holistic Review of Soft Computing Techniques

Received: 15 February 2017    Accepted: 17 March 2017    Published: 10 April 2017
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Abstract

Due to notable technological convergence that brought about exponential growth in computer world, Soft Computing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

DOI 10.11648/j.acm.20170602.15
Published in Applied and Computational Mathematics (Volume 6, Issue 2, April 2017)
Page(s) 93-110
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Intelligence, Soft Computing, Hard Computing, Hybrid Computing, Neural Network, Fuzzy Logic, Evolutionary Computation, Ant Colony Algorithm

References
[1] Aahan, B. (2016). Computational Intelligence, Granular Computing and Soft Computing. (IJCSIT) International Journal of Computer Science and Info. Technologies, Vol. 7 (1), 2016, 197-200. ISSB: 0975-9646.
[2] Adebiyi, A. A., Ayo, C. K., Adebiyi, M. O. and Otokiti, S.O. (2012). An Improved Stock Price Prediction using Hybrid Market Indicators. African Journal of Computing & ICT, ISSN 2006-1781.
[3] Agangiba, W. A and Agangiba, M. A. (2013). Mobile Solution for Metropolitan Crime Detection and Reporting, Journal of Emerging Trends in Computing and Information Sciences, Vol. 4, No. 12, ISSN 2079-8407.
[4] Ahmad, M. I. (2004). Fuzzy Logic for Embedded Systems Applications, Newnes is an imprint of Elsevier Science, Elsevier Science (USA). ISBN: 0-7506-7699-X.
[5] Ahmet, Y. (2009). Soft computing in medicine. Journal homepage: www.elsevier.com.
[6] Akgündogdu, A. (2012). Breast cancer classification with genetic programming. Intl. Journal of Elect, Mechanical and Mechatronics Engg. Vol. 2, Num. 1 pp. (72-78).
[7] Akhter, M. and Ahamad, G. (2012). Detecting Telecommunication Fraud using Neural Networks through Data Mining; Intl. Journal of Scientific & Engrg Research, Vol. 3, Issue 3, ISSN 2229-5518.
[8] Akumua, C. E., Woodsc, M., Johnsonb, J. A., Pittd, D. G., Uhligb, P. and McMurraye, S. (2016). GIS-fuzzy logic technique in modeling soil depth classes. Geoderma, Vol. 283, pp 78–87.
[9] Ali G., Hossein, S. and Hasan, A. (2013). Robust design of multimachine power system stabilizers using fuzzy gravitational search algorithm. International Journal of Electrical Power & Energy Systems. Vol. 51, Pp 190–200.
[10] Alikhania, R. and Adel, A. (2015). A hybrid fuzzy satisfying optimization model for sustainable gas resources allocation. Journal of Cleaner Production. Vol. 107, pp. 353–365.
[11] Alireza, M. B., Gai-Ge, W., Hamed, B., Amir, H. and Amir, H. G. (2004). Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete. Hindawi Publishing Corporation, Mathematical Problems in Engineering. http://dx.doi.org/10.1155/2014/474289.
[12] Alireza, M., Roozbeh, R., Saeidreza, M. and Masoud, B. (2016). A Load Balancing Routing Mechanism Based on Ant Colony Optimization Algorithm for Vehicular Adhoc Network. Intl. Journal Network and Computer Engineering. ISSN 0975-6485, Vol. 7, No1, pp. 1-10.
[13] Aloysius G., Rajakumar, B. R. and Binu, D. (2012) "Genetic algorithm based airlines booking terminal open/close decision system".
[14] Amin T. J., Meng Joo Era, Xiang Lib,Beng Siong Limb. (2016). Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis, Neuro-computing, Vol. 196, Pp 31–41.
[15] Amir, A. A., Afshin, G., and Siti, M. S. (2009). Advances of Soft Computing Methods in Edge Detection. Int. J. Advance. Soft Comput. Appl., Vol. 1, No. 2. ISSN 2074-8523; ICSRS Publication. www.i-csrs.org.
[16] Anbazhagan, S., and Ponmuthuramalingam. K. (2014); Neural Networks Based Pattern Recognition Using Perceptron Model; International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), IJETCAS 14-761; Pp 171, ISSN (Online): 2279-0055.
[17] Asadi, R., Mustapha, N., Sulaiman, N. and Shiri, N. (2009) “New Supervised Multi Layer Feed Forward Neural Network Model to Accelerate Classification with High Accuracy”, European Journal of Scientific Research, Vol. 33, No. 1, pp.163-178.
[18] Atsalakis, G. S. and Kimon, P. V. (2009). "Surveying stock market forecasting techniques. Part II: Soft computing methods." Expert Systems with Applications ISBN: 5932-5941.
[19] Ayesha, T. (2016). A Survey on Using Fuzzy Logic in Edge Selection of XTC Algorithm for MANET. International Journal of Innovative Research in Computer and Communication Engineering. Vol. 4, Issue 3.
[20] Baba, N., Inoue, N., and Yanjun, Y. (2002). Utilization of Soft Computing Techniques for Constructing Reliable Decision Support Systems for Dealing Stocks. IJCNN'02: Proceedings of the 2002 International Joint Conference on Neural Networks. Honolulu, Hawaii.
[21] Bajpai, P. and Kumar, M. (2016). Genetic Algorithm – an Approach to Solve Global Optimization Problems, Indian J. Computer Sci. and Eng., Vol. 1, No. 3, pp. 199-206.
[22] Bara, A. and Haidar, S. K. (2016). New multi-objective evolutionary framework for community mining in dynamic social networks, Swarm and Evolutionary Computation. Vol. 31, Pp. 90–109.
[23] Bell, J. E. and McMullen, P. R. (2008). “Ant colony optimization techniques for the vehicle routing problem”, Advanced Engineering Informatics. 18, 41-48.
[24] Berthouze, L. and Lorenzi, A. (2008). Bifurcation angles in ant foraging networks: A trade-off between exploration and exploitation? J Theoretical Biol. 12: 113–122.
[25] Boiocchi, R., Iglesias, M., Vangsgaard, A. K., Gernaey, K. V. and Sin, G. (2016). Aeration control by monitoring the microbiological activity using fuzzy logic diagnosis & control. Journal of Process Control Vol.30, Pp 22–33. http://dx.doi.org/10.1016/j.jprocont.2014.10.011.
[26] Boussaïd, I., Julien, L., and Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, Vol. 237, Pp. 82–117.
[27] Buhl, J., Hicks, K., Miller, E., Persey, S., and Alinvi, O. (2009). Shape and efficiency of wood ant foraging networks. Behav. Ecol. Sociobiol; 63: 451–460.
[28] Buket, K., Cemalettin, K. and Özer,, U. (2015). Talent management in manufacturing system using fuzzy logic approach. Comp & Ind. Engg. Vol. 86, pp. 127–136. DOI: http://dx.doi.org/10.1016/j.cie.2014.09.015.
[29] Burnwal, A. P., Abhishek, K. and Das, S. K. (2013). “Assessment of fuzzy set theory in different paradigm”, Intl. Journal of Advanced Technology & Engineering Research, pp. 16-22.
[30] Burnwal, A. P., Abhishek, K. and Das, S. K. (2014). “Survey on Application of Artificial Intelligence Techniques,” International Journal of Engineering Research and Management, Vol-1, Issue-5, pp 215- 219.
[31] Cassillas, J., Cordon, O. and Viana, I. F. (2005) “Learning cooperative linguistic rules using the best–worst ant system algorithm,” Int. J., vol. 20, pp. 433– 452.
[32] Chakravarthy, N., Raja K., and Avadhan, P. S. (2011). A Novel Approach For Password Authentication Using Bidirectional Associative Memory, Advanced Computing: An International Journal (ACIJ), Vol.2, No.6.
[33] Chandra, P., Udayan, G. and Apoorvi, S. (2015). A non-sigmoidal activation function for feedforward artificial neural networks, Neural Networks, Intl Joint Conf, DOI: 10.1109/ijcnn.2015.7280440. ISSN: 2161-4407.
[34] Chen, A., Chianglin, C. and Chung, H. (2001). Establishing an Index Arbitrage model by applying Neural Networks method - A case study of Nikkei 225 Index. Intl Journal of Neural Systems. 11(5): p. 489-496.
[35] Chen, C. A., Li, Y. C., Lin, F. Y., Yu, C. F., Huang, H. W., and Chiu, J. S. (2007). Neuro-fuzzy technology as a predictor of parathyroid hormone level in hemodialysis patients, Tohoku Journal of Experimental Medicine. Pp. 81–87.
[36] Chengguang, L., Quanxi, S., Xiaohong, C., Zhaoli, W., Xiaowen, Z., Bing, Y. D. and Lilan, Z. (2016). Flood risk zoning using a rule mining based on ant colony algorithm. Journal of Hydrology. Vol. 542, Pp 268–280. http://dx.doi.org/10.1016/j.jhydrol.2016.09.003.
[37] Cheung, W. M. and Kaymak, U. (2007). A fuzzy logic based trading system, in Proc. of the Third European Symposium on Nature inspired Smart Information Systems, St. Julians, Malta, Pp. 141-148.
[38] Chieh-Yuan, T., Hui-Ting, C. and Ren, J. K. (2017). An Ant colony based optimization for RFID reader deployment in theme parks under service level consideration. Tourism Mgt. DOI:http://dx.doi.org/10.1016/j.tourman.2016.10.03. Vol. 58, Pp. 1–14.
[39] Christopher, L. and Huosheng, H. (2001). Using Genetic Programming to Evolve Robot Behaviours. Proceedings of the 3rd British Conference on Autonomous Mobile Robotics & Autonomous Systems, Manchester.
[40] Clóvis M. C., Alexandre, R., Maurício, C. M., Dorotéa, V G., Germano L., Jair, M. A., and Claudio, R. T. (2015). Application of Paraconsistent ANN in Statistical Process Control acting on voltage level monitoring in Electrical Power Systems.http://ieeexplore.ieee.org/document.
[41] Colorni, A., Dorigo, M. and Maniezzo, V. (1991). “Distributed optimization by ant colonies,” in Proceedings of the First European Conf. on Artificial Life. Pp. 134–42.
[42] Costa, D. and Hertz, A. (1997). Ants can colour graphs. Journal of Operational Research Society. 48:295–305.
[43] Cox, E. (2005). Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration, the Morgan Kaufmann Series, CA.
[44] Das, S. K., Abhishek, K., Das, B. and Burnwal, A. P. (2013a). “Ethics of Reducing Power Consumption in Wireless Sensor Networks using Soft Computing Techniques.”, International Journal of Advanced Computer Research”, Vol. 3, No. 1, Issue. 8, pp. 301-304.
[45] Das, S. K., Abhishek, K., Das, B. and Burnwal, A. P. (2013b). On soft computing techniques in various areas”. http://airccj.org/CSCP/vol3/csit3206.pdf. ACER CS & IT. Pp. 59–68.
[46] Das, S. K., Das B. and Burnwal, A. P. (2014a). “Intelligent energy competency routing scheme for wireless sensor networks”, International journal of research in computer applications and robotics, Vol. 2, No. 3, pp. 79-84.
[47] Das, S. K., Sachin, T. and Burnwal, A. P. (2014b).Some relevant field of Soft Computing methodology. International Journal of Research in Computer Applications and Robotics ISSN 2320-7345, www.ijrcar.com, Vol.2 Issue.6, Pg. 1-6.
[48] De Campos, L. M., Fernandez-Luna J. M., Amez, J. A. (2002a) Ant colony optimization for learning Bayesian networks. Intl Journal Approx Reason; 31(3): 291–311.
[49] De Campos, L. M., Gamez, J. A., and Puerta, J. M. (2002). Learning Bayesian networks by ant colony optimization: searching in the space of orderings. Mathware Soft Comput; 9(2–3):251–268.
[50] De Jong, K. A. (2006). Evolutionary Computation: A Unified Approach. The MIT Press Cambridge, Massachusetts London, England. ISBN 0-262-04194-4.
[51] Delcroix V., Maalej, M, and Piechowiak, S. (2011).Bayesian Networks versus Other Probabilistic Models. International Journal on Artificial Intelligence Tools, Vol. 16, (doi: 10.1142/S0218213007003345). No. 03, pp. 417-433.
[52] Devadoss, A. V. and Ligori, A. A. (2013). Adoption of Neural Network in forecasting the trends of stock market. Vol. 02, Pp 387-392 International Journal of Computing Algorithm. Integrated Intelligent Research (IIR).
[53] Dharmistha, M. and Vishwakarma, D. (2012). Genetic Algorithm based Weights Optimization of Artificial Neural Network, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 1, Issue 3, Copyright to IJAREEIE www.ijareeie.com, ISSN: 2278 – 8875.
[54] Donatia, A. V., Andrea, E. R., Norman, C., Gambardella, L. M., Lepori, D., Montemanni, R., Piero, P and Marco, Z. (2011). Planning and optimization of vehicle routes for fuel oil distribution. http://people.idsia.ch/~luca/Dyvo.pdf.
[55] Donatia, A. V., Montemanni, R., Casagrande, N., Rizzoli, A. E. and Gambardella, L. M. (2008). “Time dependent vehicle routing problem with a multi ant colony system”, European Journal of Operational Research, 185 (3), 1174-1191.
[56] Dorigo M. and Gambardella, L. M. (1997). “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evol. Comput., Vol. 1, no. 1, pp. 53–66.
[57] Dorigo, M. and Di, C.G. (1999) “Ant colony optimization: a new meta-heuristic,” in Proceedings of the 1999 Congress on Evolutionary Computation, Vol. 2, pp. 1470–1477.
[58] Dorigo, M., Di, C. G. and Gambardella, L.M. (1999) “Ant algorithms for discrete optimization,” Artificial Life, vol. 5, pp. 137–172.
[59] Dorigo, M., Maniezzo, V. and Colorni, A. (1996) “The ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man and Cybernetics, vol. 26, pp. 29–41.
[60] Dorigo, M., Maniezzo, V. and Colorni, A. (1991). Positive feedback as a search strategy, Tech. Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Italy.
[61] Dourra, H and Siy, P (2001). Stock Evaluation Using Fuzzy Logic. Intl Journal of Theoretical and Applied Finance 04, 585.
[62] Dourra, H. and Siy, P. (2002). Investment using Technical Analysis and Fuzzy Logic. Fuzzy Sets and Systems. 127: p. 221-240.
[63] Efe, M, (2008). “Novel neuronal activation functions for feed forward neural networks,” Neural Process Letter 28:63–79.
[64] Elnaggar, E. O., Ramadan, R. A., and Magda, B. F. (2015). WSN in Monitoring Oil Pipelines Using ACO and GA. Procedia Computer Science. Vol. 52, pp. 1198-1205. doi:10.1016/j.procs.2015.05.158.
[65] Farman, A., Kyung-Sup, K., and Yong-Gi, K. (2016). Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification. Applied Soft Computing, Vol. 47, pp. 235–250.
[66] Feng, J., Yaguo, L., Jing, L., Xin, Z., and Na, L. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing. Vol. 72–73, Pp. 303–315.
[67] Feng, X. R., Feng, X. J. and Liu, D. (2013). The Application of Ant Colony Optimization Algorithm in the Flight Landing Scheduling Problem, Applied Mechanics and Materials, Vols. 411-414, pp. 2698-2703.
[68] Fitzgerald, J., Ryan, C., Medernach, D and Krzysztof, K. (2015). "An Integrated Approach to Stage 1 Breast Cancer Detection”.
[69] Fogel, D. B. (1991). System Identification through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press.
[70] Fogel, D. B., Wasson, E. C., Boughton, E. M. and Porto, V. W. (1997) “A step toward computer-assisted mammography using evolutionary programming and neural networks,” Cancer Letters, Vol. 119, pp. 93-97.
[71] Fogel, D. B., Fogel, L. J. and Atmar, W. (1993). Evolutionary programming for ASAT battle management. Proc. 27th Asilomar Conf. on Signals, Systems, and Computers, A. Singh (ed.), IEEE Computer Society Press, Los Alamitos, CA, pp. 617-621.
[72] Fogel, L. J. (1962). Autonomous automata. Industrial Research 4, 14-19.
[73] Fogel, L. J., Owens, A. J., and Walsh, M. J. (1966). Artificial intelligence. Artificial Intelligence through Simulated Evolution. John Wiley & Sons.
[74] Funes, E., Allouche, Y., Beltrán, G. and Jiménez, A. (2015). A Review: Artificial Neural Networks as Tool for Control Food Industry Process, Journal of Sensor Technology. Pp. 28-43. Published Online in SciRes. http://www.scirp.org/journal/jst.
[75] Ghizlane, B., Jaouad, B. and Ahmed, E. L. and Hilali, A. (2011). Improved Ant Colony Algorithm to Solve the Aircraft Landing Problem. Int. Journal of Computer Theory and Engineering, Vol. 3, No. 2. ISSN: 1793-8201.
[76] Gill, M. A. C. and Zomaya, A.Y. (1998), “A cell decomposition-based collision avoidance algorithm for robot manipulators,” Cybernetics and Systems, vol. 29, pp. 113–35.
[77] Giraud, B., Lapedes, A., Lon, C. and Lemm, J. (1995). “Lorentzian neural Nets,” Neural Net 8(5):757–767.
[78] Gomes, G. S., Ludermir, T. B. (2008). “Complementary log-log and probit: activation functions implemented in artificial neural networks,” in: 8th Int. conf. on hybrid intelligent Systems,” IEEE Comp Society, pp 939–942.
[79] Gonzalez-Pardoa, A., Jason J. J. and Camachoa, D. (2017). ACO-based clustering for Ego Network analysis. Future Generation Comp. Systems. Vol 66, pp. 160–170. DOI: http://dx.doi.org/10.1016/j.future.2016.06.033.
[80] Gorbil, G. and Gelenbe, E. (2013). “Disruption tolerant communications for large scale emergency evacuation,” in Proc. 11th IEEE Inter. Conf. on Pervasive Computing and Communications Workshops.
[81] Graupe, D. (2007). Principles of artificial neural networks. 2nd Edition. Advanced Series on Circuits and Systems – Vol.6. World Scientific Publishing Co. Pte. Ltd.
[82] Günther, F. and Fritsch, S. (2010). Neuralnet: Training of Neural Networks, the R Journal Vol. 2/1. ISSN 2073-4859.
[83] Guruprasad, R. and Behera, B. K. (2010). Soft Computing in Textiles. Indian Journal of fibre and texile research. Vol. 35. Pp. 75-84.
[84] Hans-Joachim, S., Lixiang, L., Haipeng, P., Jürgen, K. and Yixian, Y. (2014). Chaos–order transition in foraging behavior of ants. Proc Natl Acad Sci U S A. 2014 Jun 10; 111(23): 8392–8397.
[85] Hartman, E., Keeler, J. and Kowalski, D. (1990). “Layered neural networks with Gaussian hidden units as universal approximations,” Neural Comput Appl 2(2): 210–215.
[86] Hayat, T. and Knanim, K. M. J. (2014). “New Fuzzy CSP for an Optimized Mobile Robot’s Path Tracking using Genetic Algorithms”, International Journal of Computer and Info Tech. ISSN: 2279 – 0764, Vol.3, Pp.523-531.
[87] Heckera, F. T., Stankea, A., Beckerb, T. and Hitzmanna, B (2014). Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery. Expert Systems with Applications, Vol. 41, Issue 13, pp 5882–5891.
[88] Heinonen, J. and Pettersson, F. (2007). “Hybrid ant colony optimization and visibility studies applied to a job-shop scheduling problem”, Applied Mathematics and Computation 187 (2), 989-998.
[89] Helio, J. C. B. (2013). Ant Colony Optimization - Techniques and Applications ISBN 978-953-51-1001-9, 212 pages, Publisher: InTech, under CC BY 3.0 license. DOI: 10.5772/3423.
[90] Herbers, J. M. (1983). Social organization in Leptothorax ants: Within and between-species patterns. Psyche (Stuttg): 90(4): 361–386.
[91] Holland, J.H. (1975). Adaptation in Natural and Artificial Systems, Ann Arbor: University of Michigan Press.
[92] Holldobler, B. and Wilson, E. O. (1990). The Ants, Berlin: Springer Verlag.
[93] Howlett, R.J., Zoysa, M.M., Walters, S.D. and Howson, P.A. (1999). Neural Network Techniques for Monitoring and Control of Internal Combustion Engines. Presented at Int. Symposium on Intelligent Ind. Automation, Genova, Italy.
[94] Hua M., Haibin Z., Zhigang H., Wensheng T., Pingping, D. (2017). Multi-valued collaborative QoS prediction for cloud service via time series analysis, Future Generation Computer Systems. Vol. 68, Pp. 275–288.
[95] Humayun, K. S. and Zhang, Y. (2007). Hopfield Neural Networks—A Survey, Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Corfu Island, Greece. Pp. 125-130.
[96] Isvarya L., Visalakshi, P., and Karthikeyan, N. K. (2011), "Intelligent Schemes for Bandwidth Allocation in Cellular Mobile Networks" Intl Conference on Process Automation, Control and Computing (PACC), pp 1-6.
[97] Jabbarpour, M. R., Hossein, M., Rafidah M. N., Nor, B. A., and Norazlina, K. (2014). "Ant colony optimization for vehicle traffic systems: applications and challenges." Intl Journal of BioInspired Computation 6.1: 32-56.
[98] Jang, J. S. R., Sun, C.T. and Mizutani, E. (1997). Neural-Fuzzy and soft Computing. Prentice Hall.
[99] Jaraiz-Simon, M. D., Gomez-Pulido, J. A. and Vega-Rodriguez, M. A. (2016). Embedded intelligence for fast QoS-based vertical handoff in heterogeneous wireless access networks. Pervasive and Mobile Computing, Vol. 19. Pp. 141–155.
[100] Javier, J. S., Moreno, M. G. and Royo, R. E. (2008). Evolutionary Computation Applied to Urban Traffic Optimization. Advances in Evolutionary Algorithms, Book edited by: Witold Kosiński, ISBN 978-953-7619-11-4. I-Tech Education and Publishing, Vienna, Austria.
[101] Jayasinghe, J. W., Anguera, J. and Uduwawala, D. N. (2013). A high-directivity microstrip patches antenna design by using genetic algorithm optimization. Progress in Electromagnetics Research C, vol. 37, p. 131-144.
[102] Jayasinghe, J. W., and Uduwawala, D. N. (2012). A broadband triple-frequency patch antenna for WLAN applications using genetic algorithm optimization. In 7th IEEE Intl Conf. on Ind. and Info. System. Pp. 1-4.
[103] Jeroen, S.B., Klaus-Peter, A., Alexander, B. and Walter, K. (2016). Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic. Elsevier: Artificial Intelligence in Medicine, Vol. 69. Pp 33–41.
[104] Jia, Y., Wang, P. and Yue, L. (2004).Study of manufacturing system based on neural network multi-sensor data fusion and its application. IEEE Journal Explore. DOI: 10.1109/RISSP.2003.1285729.
[105] Jibendu, K. M., Gahan, P. and Nayak, B. B. (2010) Artificial Neural Networks – an application to stock market volatility. Intl Journal of Eng’g Science and Technology Vol. 2(5), 1451-1460.
[106] Jie, S., Wen-jun X., Zhi-qiang, H., Kai, N., and Wei-ling, W.U. (2009). Resource allocation based on genetic algorithm for multi-hop OFDM system with non-regenerative relaying. The Journal of China, Universities of Posts and Telecoms, Vol. 16, Issue 5. Pp: 25-32.
[107] Jinde, Z., Haiyang, P. and Junsheng, C. (2017). Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing. Vol. 85, Pp: 746–759.
[108] Juang, C. F., Lu, C. M., Lo, C. and Wang, C. Y. (2008). “Ant colony optimization algorithm for fuzzy controller design and its FPGA implementation,” IEEE Trans. Ind. Electron., Vol. 55, No. 3. Pp. 1453–1462.
[109] Kandel, A. (1992). Fuzzy expert systems, CRC Press, Inc. Boca Raton, FL, USA. ISBN: 0-8493-4297-X.
[110] Kapoor, N., Russell, M., Stojmenovic, I. and Zomaya, A. Y. (2002) “A genetic algorithm for finding the page number of interconnection networks,” Journal of Parallel and Distributed Computing, Vol. 62, Pp. 267–83.
[111] Karray, F. O. and De Silva, C.. (2004). Soft computing and Intelligent Systems Design: Theory, Tools and Applications. Addison-Wesley.
[112] Kayri, M. and Çokluk, Ö. (2010) “Using Multinomial Logistic Regression Analysis in Artificial Neural Network: An Application”, Ozean Journal of Applied Sciences Vol. 3, No. 2.
[113] Khalid, S. and Dwivedi, B. (2013).Comparative Critical Analysis of SAF using Soft Computing and Conventional Control Techniques for High Frequency (400 Hz) Aircraft System. Proceeding of IEEE- CATCON Conf: 100-110.
[114] Khashei, M., Hejazi, S. R. and Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy Sets and System, 259(7), 769-786.
[115] Khashei, M., Bijari, M. and Ardali, G. A. R. (2009). Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs). International Journal of Neurocomputing, 72, 956-967.
[116] Khatiba, E. J., Barcoa, R., Gómez-Andradesa, A., Muñoza, P., and Serranob, I. (2015). Data mining for fuzzy diagnosis systems in LTE networks. Expert Systems with Appls. Vol. 42, Issue 21, Pp. 7549–7559.
[117] Kiran, N. R and Ravi, V. (2007). “Software reliability prediction by soft computing techniques”, the journal of system and software.
[118] Kosko, B. (1991) Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs, NJ.
[119] Koza, J. (1992) Genetic Programming. MIT Press, Cambridge, MA.
[120] Koza, J. R., Bennett, F. H., Andre, D. and Keane, M. A. (1998). Automatic creation of computer programs for designing electrical circuits using genetic programming. Computational Intelligence in Software Engineering. Pp. 127-149. doi: 10.1142/9789812816153_0005.
[121] Krzysztof, K. and Pawlak, M. (2015). "Genetic Programming with Alternative Search Drivers for Detection of Retinal Blood Vessels".
[122] Kumar, A., Ramesh K. S. and Burnwal, A. P. (2015). Energy Consumption Model in Wireless Ad-hoc. Networks using Fuzzy Set Theory, Vol-2, Issue-2, Pp. 419-426 ISSN: 2394-5788, www.gjar.org.
[123] Kun-Lin H. (2010). Incorporating ANNs and statistical techniques into achieving process analysis in TFT-LCD manufacturing industry. Robotics and Computer-Integrated Manufacturing, Vol. 26, Issue 1, Pp. 92–99. DOI: http://dx.doi.org/10.1016/j.rcim.2009.04.019.
[124] Latty, T., Ramsch, K., Ito, K., Nakagaki, T. and Sumpter, D. J. T. (2011). Structure and formation of ant transportation networks. J R Soc Interface; 8:1298–1306.
[125] Lee, C. C. (1990). Fuzzy Logic in Control Systems: Fuzzy Logic Controller - Part I & II. IEEE Transactions on Systems, Man & Cybernetics, Vol. 20, Pp. 404 - 435.
[126] Lei, X., Ya-ping, L., Qian-mu, L., Yu-wang, Y., Zhen-min, T. and Xiao-fei, Z. (2015). Proportional fair resource allocation based on hybrid ant colony optimization for slow adaptive OFDMA system. Information Sciences, Vol. 293. Pp. 1–10. DOI: http://dx.doi.org/10.1016/j.ins.2014.09.028.
[127] Letendre, K. (2010). "Simulating the Evolution of Recruitment Behavior in Foraging Ants." Diss. University of New Mexico.
[128] Leung, H. and Haykin, S. (1999). “Rational function neural network,” Neural Comput Appl 5(6):928–938, 1993.
[129] Liao, T. W (2008). Enterprise Data Mining: A Review and Research Directions. Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Pp. 1-109. DOI: 10.1142/9789812779861_0001.
[130] Liebergeld, S., Lange, M. and Mulliner, C. “Nomadic honeypots (2013): A novel concept for smartphone honeypots,” in Proc. W’shop on Mobile Security Technologies (MoST’13), together with 34th IEEE Symp. On Security and Privacy.
[131] Lokman, H. H., Moghavvemi, M., Haider, A. F. and Otto S. (2013). Current state of neural networks applications in power system monitoring and control. Intl. Journal of Elect.Power & Energy Systems. Vol. 51, Pp. 134–144. http://dx.doi.org/10.1016/j.ijepes.2013.03.
[132] Ma, L. and Khorasani, K. (2005). Constructive feed forward neural networks using hermite polynomial activation functions,” IEEE Trans Neural Net 16(4):821–833.
[133] Mamdani, E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Transactions on Computers 26(12): 1182–1191.
[134] Mamdani, E. H. and Assilian, S. (1975)"An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, Vol. 7, No. 1, pp. 1-13.
[135] Maniezzo, V., Colorni, A. and Dorigo, M. (1999). The Ant System applied to the quadratic assignment problem. IEEE Trans Data Knowl Eng; 11(5): 769–778.
[136] Mankad, K. B. (2014a). An Architectural Perspective of Soft Computing Methods, International Journal of Emerging Research in Management and Technology. ISSN: 2278-9359. Vol. 4, Issue-2.
[137] Mankad, K. B. (2014b)” The Significance of Genetic Algorithms in Search, Evolution, Optimization and Hybridization: A Short Review”, International Journal of Computer Science and Business Informatics, Vol. 9, No. 1, pp. 103-115.
[138] Mankad, K. B. and Sajja, P. S. (2013). “The Impact of Genetic Fuzzy Modeling for Machine Intelligence”, Information Technology Research Journal. Vol. 3(1), pp. 1 – 8.
[139] Mar J., Yow-Cheng, Y., I-Fan, H. (2010), "An ANFIS-IDS against de-authentication DOS attacks for a WLAN," Int'l Symp. On Info. Theory and its app, pp 548-553.
[140] Marghny M., Al-Mehdhar, A., and Bamatraf, M. (2013). Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification, Intelligent Info. Mgt, 5, 25-33 http://dx.doi.org/10.4236/iim.2013.51004.
[141] Martens, D., De Backer M., and Haesen, R. (2007). Classification with ant colony optimization. IEEE Trans. Evol. Comput; 11(5): 651–665.
[142] Matej, P. and Marko R. (2014). Handling numeric attributes with ant colony based classifier for medical decision making. Expert Systems with Applications, Vol. 41, Issue 16, Pp. 7524–7535. DOI: http://dx.doi.org/10.1016/j.eswa.2014.06.017.
[143] McCarthy, J. (2007).What is Artificial Intelligence? steam.stanford.edu:/u/ftp/jmc/ whatisai.tex: begun Sat Nov 23 10:30:17 1996
[144] Mehdi, H., Seyed, A. M., Amir, A. D. and Yones, K. (2016). Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods. Soil and Tillage Research, Vol. 157, pp. 32–42. DOI: http://dx.doi.org/10.1016/j.still.2015.11.004.
[145] Michel, R., Middendorf, M. (1999). An ACO algorithm for the shortest supersequence problem. In: Corne D, Dorigo M, Glover F, editors. New ideas in optimization. London: McGraw Hill. Pp. 51–61.
[146] Ming-Shyan, W., Seng-Chi, C., Po-Hsiang, C., Shih-Yu, W. and Fu-Shung, H. (2015). Neural Network Control-Based Drive Design of Servomotor and Its App. to automatic Guided Vehicle. Mathematical Problems in Engrg. Hindawi Publishing Corp, Article ID 612932.
[147] Mitchell, M. (1996). An Introduction to Genetic Algorithms, Cambridge, MA: MIT Press.
[148] Mitra, S. and Shankar, U. (2015). Medical image analysis for cancer management in natural computing framework. Information Sciences, Vol. 306. Pp: 111–131.
[149] Mohamed, M. M. (2010). Forecasting stock exchange movements using neural networks: empirical evidence from Kuwait. Journal of Expert Systems with Applications, 27(9), 6302–6309.
[150] Mom, J. M. and Ani, C. I. (2013). ”Application of self-organizing map to intelligent analysis of cellular networks”, ARPN Journal of Engineering and Applied Sciences, Vol. 8, No. 6, pp. 407 – 412. ISSN 1819-6608. (http://www.arpnjournals.com/jeas/research_papers/rp_2013/jeas_0613_896.pdf).
[151] Mom, J. M., and Ani, C. I (2012). “An integrated block-oriented simulation model for estimating cell loss rate in ATM networks”, Pacific Journal of Sci& tech, Vol. 13, No. 1, pp.287-291. (http://www.akamaiuniversity.us/PJST13_1_287.pdf).
[152] Mom, J. M., Tarkaa, N. S. and Ani, C. I. (2013) “The effects of propagation environment on cellular network performance,” American Journal of Engineering Research, Vol. 2, Issue 9. Pp. 31 - 36. E-ISSN 2320-0847, p-ISSN 2320-0936. (http://www.ajer.org).
[153] Morteza, H. Y., Vahid, R., Hamid, R. D. (2014). A Survey on Evolutionary Computation: Methods and Their Applications in Engineering. Modern Applied Science 10(11): 131. DOI: 10.5539/mas.v10n11p131.
[154] Moustafa, M., Habib, I. and Naghshineh, M. (2011). Wireless resource management using genetic algorithm for mobiles equilibrium. Vol. 37, Pp. 631–643.
[155] Mucientes, M. and Casillas, J. (2007). “Quick design of fuzzy controllers with good interpretability in mobile robotics,” IEEE Trans. Fuzzy Syst., vol. 15, no. 4, pp. 636–651.
[156] Namita, S. and Vineet, R. (2012). Ant colony optimization with classification algorithms used for intrusion detection. In International Journal of Computational Engineering and Management, IJCEM. Vol. 7, Pp. 54–63.
[157] Nishitha, T. and Amarnath, E. (2014). Routing in Ad Hoc Networks Using Ant Colony Optimization. Fifth International Conference on Intelligent Systems, Modeling and Simulation. 2166-0662/14. IEEE. DOI 10.1109/ISMS.2014.100.
[158] Otero, F., Freitas A. A., Johnson C. G. (2008). An ant colony classification algorithm to cope with continuous attributes. In: Dorigo M, et al., editors. Vol. 5217, Optimization and Swarm Intelligence: 6th International Conference, ANTS 2008, Lecture Notes in Computer Science. Heidelberg: Springer. pp. 48–59.
[159] Pao, Y. H, (1989). Adaptive pattern recognition and neural networks, 2nd edition. Addison-Wesley, New York.
[160] Parpinelli, R. S., Lopes, H. S. and Freitas, A. A. (2002). “Data mining with an ant colony optimization algorithm,” IEEE Trans. Evol. Comput, vol. 6, no. 4, pp. 321–332.
[161] Patrascu, M. (2015). "Genetically enhanced modal controller design for seismic vibration in nonlinear multi-damper configuration". Preceding of industrial Mech. Part I: Journal of Systems and Control Engineering. 229 (2): 158–168. DOI: 10.1177/0959651814550540.
[162] Pearce, G., Wong, J., Lela, M., Salah, A., and Gulua, N. B. (2015), Artificial Neural Network and Mobile Applications in Medical diagnosis" International Conference on Modelling and Simulation, 17th UKSIM-AMSS.
[163] Perna, A., Granovskiy, B., Garnier, S., Nicolis, S., Labédan, M., Theraulaz, G., Fourcassié, V., and Sumpter, J. T. (2012). “Individual Rules for Trail Pattern Formation in Argentine Ants (Linepithema humile)”.
[164] Ping-Feng, P., Kuo-Chen H., and Kuo-Ping, L. (2014).Tourism demand forecasting using novel hybrid system. Expert Systems with Appl. Vol. 41, Issue 8, pp. 3691–3702. DOI: http://dx.doi.org/10.1016/j.eswa.2013.
[165] Pinto, P. C., Nagele, A. and Dejori, M. (2009). Using a local discovery ant algorithm for Bayesian network structure learning. IEEE Trans. Evol. Comput; 13(4): 767–779.
[166] Prakash, S., Arnaud, Q. and Óscar, C. (2013). A multi-objective EP framework for graph-based data mining. Info Sciences, Vol. 237, Pp. 118–136. http://dx.doi.org/10.1016/j.ins.2013.02.014.
[167] Praveen, R. S. and Tai-hoon, K. (2009). Application of Genetic Algorithm in Software Testing. Intl Journal of Software Eng’g and Its Appls Vol. 3, No. 4.
[168] Puja, G. and Neha, K. (2013). An Introduction of Soft Computing Approach over Hard Computing, International Journal of Latest Trends in Engineering and Technology (IJLTET), Vol. 3, Issue. ISSN: 2278-621X.
[169] QiSen, C., Defu, Z., Bo, W. and Loung, C. H. (2013). A Novel Stock Forecasting Model based on Fuzzy Time Series and Genetic Algorithm. Procedia Computer Science. Vol. 18, Pp 1155-1162. Int. Conf. on Computational Science. DOI:10.1016/j.procs.2013.05.281.
[170] Rajappa, V., Biradar, A.,Panda, S. (2008) “Efficient Software Test Case Generation Using Genetic Algorithm Based Graph Theory,” First International Conference on Emerging Trends in Engineering and Technology, ICETET '08, pp.298-303, 2008.
[171] Rajesh, K. A. (2015). Optimization: Algorithms and Applications. Taylor & Francis Group, LLC. ISBN: 978-1-4987-2115-8 (eBook - PDF).
[172] Rajkumari, B. D, Esha, B., Oinam, B. D., Smriti, P. and Mand, R. S. (2014). Survey on evolutionary computation tech techniques and its application in different fields. Intl Journal on Information Theory (IJIT), Vol.3, No.3. DOI: 10.5121/ijit.2014.3308 73.
[173] Rakhee, M. and Srinivas, B. (2016). Cluster Based Energy Efficient Routing Protocol Using ANT Colony Optimization and Breadth First Search. Procedia Computer Science, Vol. 89, Pp. 124–133.
[174] Rathikaa, P. D. and Sophiab, S. (2016). A distributed scheduling approach for QoS improvement in cognitive radio networks. Computer andElectrical Engineering DOI:http://dx.doi.org/10.1016/j.compeleceng.2016.08.013.
[175] Reid, C. R., Sumpter, D. J. T, and Beekman, M. (2011). Optimization in a natural system: Argentine ants solve the towers of Hanoi. J Exp Biol.; 214:50–58.
[176] Resnick, M. (1994). Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. Cambridge, MA: MIT Press.
[177] Sachin, S. and Kumar, G. (2011). Object Classification through Perceptron Model using LabView, ISSN: 2230-7109(Online). ISSN: 2230-9543(Print) IJECT Vol. 2, Issue 3.
[178] Saifullah, K. (2016). Comparison of Soft Computing Techniques applied in High Frequency Aircraft System. Indonesian Journal of Electrical Engineering and Informatics (IJEEI). Vol. 4, No. 2, pp. 102~111. ISSN: 2089-3272, DOI: 10.11591/ijeei.
[179] Samira, K., Mohsen, A. B., Daliri, S. Z., Shahaboddin, S. and Liang, S. N. (2011). "Routing in wireless sensor network based on soft computing technique." Scientific Research and Essays 6.21: 432-4441.
[180] Saravanan, K and Sasithra, S. (2014). Review on Classification Based on Artificial Neural Networks, Intl Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4, DOI: 10.5121/ijasa.2014.2402 11.
[181] Schwefel, H. P. (1995). Evolution and Optimum Seeking. John Wiley and Sons.
[182] Sedenka, V. and Raida, Z. (2010). Critical comparison of multi-objective optimization methods: genetic algorithms versus swarm intelligence. Radio engineering. Vol. 19, No. 3, p. 369–377.
[183] Segal, R., Kothari, M. L. and Madnani, S. (2000), “Radial basis function (RBF) network adaptive power systems stabilizer,” IEEE Trans. Power Syst., Vol. 15, pp. 722–727.
[184] Senthil, A. V. (2015). Fuzzy Expert Systems for Disease Diagnosis, IGI Global.
[185] Serag-Eldin, G., Souafi-Bensafi, S., Lee, J. K., and Chan, W. K. (2004). Web Intelligence: Web-Based BISC Decision Support System (WBISC-DSS), in: Y. Q. Zhang, et al. (Eds.), Computational Web Intelligence, Univ. of CA, Berkely, pp. 391-429.
[186] Shackelford, S. (2014). Evolutionary Algorithm for Drug Discovery – Interim Design Report, Advanced Computational Technologies. 9 Belsize Road, West Worthing, West Sussex, BN11 4RH, UK.
[187] Sharawi, M., Imane, A. S., Heshman, E., and Eid, E. (2013). "Routing Wireless Sensor Networks based on Soft Computing Paradigms: Survey." International Journal.
[188] Shelly, X. W, Wolfgang, B. (2010). The use of computational intelligence in intrusion detection systems: A review. Applied Soft Computing. Vol. 10, Issue 1. Pp. 1–35.
[189] Sibi, P. S., Allwyn, J., and Siddarth, P. (2013), Analysis of Different Activation Functions Using Back Propagation Neural Networks, Journal of Theoretical and Applied Info Tech. Vol. 47, No. 3. ISSN: 1992-8645 www.jatit.org. E-ISSN: 1817-3195 1264.
[190] Sikchi, S. S., Sikchi, S. and Ali, M. S. (2013). Design of fuzzy expert system for diagnosis of cardiac diseases. International Journal of Medical Science and Public Health; 2(1):56–61.
[191] Silva, C.A., Sousa, C. A., Runkler, T. and Sá da Costa, J. M. G. (2006). Rescheduling of Logistic Processes Using GA. ACOIFAC Proceedings. Vol. 39, Issue 3, pp. 547-552, 12th IFAC Symposium on Info. Control Problems in Manufacturing. doi:10.3182/20060517-3-FR-2903.00284.
[192] Silvaa, C.A., Sousaa, M. C. and Runkler, T. A. (2008). Rescheduling and optimization of logistic processes using GA and ACO. Engineering Applications of Artificial Intelligence. Vol. 21, Issue 3. Pp 343–352. DOI: http://dx.doi.org/10.1016/j.engappai.2007.08.006.
[193] Sim, K. M. and Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions, IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 33, no. 5, pp. 560–572.
[194] Singh, T. P. and Suraiya, J. (2012); Evolving Connection Weights for Pattern Storage and Recall in Hopfield Model of Feedback Neural Networks Using a Genetic Algorithm, International Journal on Soft Computing (IJSC) Vol.3, No.2.
[195] Skoundrianos, E. N, and Tzafestas, S. G. (2004). “Modeling and FDI of dynamic discrete time systems using a MLP with a new sigmoidal activation function,” Journal of Intelligence Robotics System 41(1): 19–36.
[196] Sönmez, F. and Bülbül, S. (2015). An intelligent software model design for estimating deposit banks profitability with soft comp. techniques. Neural network world. Vol. 25, No 3. http://ojs.nnw.cz/article/view/25.017.
[197] Soomer, M. J. and Franx, G. J. (2008) “Scheduling aircraft landings using airlines’ preferences”, European Journal of Operational Research, vol. 190 (1), 277-291.
[198] Srinivas, M. and Patnaik, L. (1994). “Genetic Algorithm: A Survey”, IEEE Computer Society, Vol. 27, No. 6, pp. 17 – 26.
[199] Stutzle, T. and Hoos, H. H. (2000). MAX–MIN Ant System. Future Generation Computer System; 16 (8): 889–914.
[200] Sudd, J. H. (1967). An Introduction to the Behavior of Ants. New York: University of Hull St Martin’s Press.
[201] Sugeno, M. (1988). Fuzzy Control. North-Holland.
[202] Sultan, H. A., and Mohammed, E. E. (2009). “Software reliability prediction using multi-objective genetic algorithm”, 978-1-4244-3806. IEEE, pp. 293-300.
[203] Sumathi, S., Hamsapriya, T. and Surekha, P. (2008). Evolutionary Intelligence: An Introduction to Theory and Applications with Matlab. Springer-Verlag Berlin Heidelberg. ISBN: 978-3-540-75158-8 e-ISBN: 978-3-540-75382-7.
[204] Tahsin, A. F. and Barbeau, M. (2013). QaASs: QoS aware adaptive security scheme for video streaming in MANETs. Journal of Information Security & Applications. Vol. 18, Issue 1, pp. 68–82.
[205] Tan, P. Y. (1994). Using Genetic Algorithm to optimize an oscillator-based market timing system in Proceedings of the Second Singapore International Conference on Intelligent Systems SPICIS'94. Singapore.
[206] Tangbin, X., Lifeng, X., Ershun, P. and Jun, N. (2016). Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems. Reliability Engineering & System Safety. DOI: http://dx.doi.org/10.1016/j.ress.2016.09.001.
[207] Tanuj, S. and Hitesh, D. M. (2016). A new approach to solve Economic Dispatch problem using a Hybrid ACO–ABC–HS optimization algorithm. Intl. Journal of Electrical Power & Energy Systems, Vol. 78, pp. 735–744. DOI: http://dx.doi.org/10.1016/j.ijepes.2015.11.121.
[208] Tarkaa, N. S., Mom, J. M. and Ani, C. I. (2011). Drop Call Probability Factors in Cellular Networks. International Journal of Scientific & Engineering Research, Vol. 2, issue 10, pp. 1 - 5. ISSN 2229-5518.
[209] Tayeb, M. B. (2013). Faults Detection in Power Systems Using Artificial Neural Network, American Journal of Engineering Research (AJER), e-ISSN: 2320-0847 p-ISSN: 2320-0936, Vol. 02, Issue-06, pp-69-75, www.ajer.org.
[210] Tecuci, G. and Boicu, M. (2002). Military Applications of the Disciple Learning Agent. Advances in Intelligent Systems for Defense. Pp: 337-376. doi: 10.1142/9789812776341_0008.
[211] Toly, C. (2017).An ANN approach for modeling the multisource yield learning process with semiconductor manufacturing as an example. Elsevier-Computers & Industrial Engineering Volume 103, Pp. 98–104.
[212] Toshinori, M. (2008). Fundamentals of the New Artificial Intelligence Neural, Evolutionary, Fuzzy and More. Springer Science and Business Media. Second Edition. ISBN: 978-1-84628-838-8 e-ISBN: 978-1-84628-839-5. DOI: 10.1007/978-1-84628-839-5.
[213] Tsukamoto, Y. (1979). An approach to fuzzy reasoning method, North-Holland. Pp. 137–149.
[214] Turksen, I. B. (2008). Fuzzy functions with Least Squared Error, Appl. Soft Comput. 8(3), pp. 1178-1188.
[215] Turksen, I. B. (2009). Fuzzy System Models, Encyclopedia of Complexity and Systems Science, pp. 4080-4094.
[216] Ubeyli, E. D. and Guler, I. (2005). Automatic detection of erthemato-squamous diseases using adaptive neuro-fuzzy inference systems. Computers in Biology and Medicine 35 (5) 421–433.
[217] Uncu, O. and Turksen, I. B. (2007) Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters, IEEE T. Fuzzy Systems 15(1), pp. 90-106.
[218] Vishal, S. and Amit, G. (2016). A modified ant colony optimization algorithm (mACO) for energy efficient wireless sensor networks. Optik – Intl. Journal for Light and Electron Optics. Vol. 127, Issue 4. Pp 2169–2172.
[219] Vose, M. D. (1999).The Simple Genetic Algorithm: Foundations and Theory. Cambridge, MA: MIT Press.
[220] Wei-Yi, L. and Kun, Y. (2011). Bayesian Network with Interval Probability Parameters. International Journal on Artificial Intelligence Tools, Vol. 20, No. 05, pp. 911-939. (doi:10.1142/S0218213011000449).
[221] Wen, C. and Ma, X. (2005) “A max-piecewise-linear neural network for function approximation,” Neuro-computing 71:843–852.
[222] Wen, Y. E, Deng-wu, M. A. and Hong-da, F. A. (2005). Algorithm for Low Altitude Penetration Aircraft Path Planning with Improved Ant Colony Algorithm. Chinese Journal of Aeronautics, Vol. 18, Issue 4, Pp. 304-309.
[223] Wolfgang, B. (2012). Evolutionary Computation and Genetic Programming, Department of Computer Science, Memorial University of Newfoundland, St. John’s, A1B 3X5, CANADA.
[224] Worrell, J. (2015) "Computational Learning Theory: University of Oxford. Presentation page of CLT course. University of Oxford.
[225] Xiao, L. Z., Wei, C., BaoJian, W. and Xuefeng, C. (2015). Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neuro-computing, Vol. 167, Pp 260–279.
[226] Yang, L., Dandan, Z., Jianquan, L. and Jinde, C. (2016). Global μ-stability criteria for quaternion-valued neural networks with unbounded time-varying delays. Information Sciences, Vol. 360, Pp. 273–288. DOI: http://dx.doi.org/10.1016/j.ins.2016.04.033.
[227] Yaralidarani, M. and Shahverdi, H. (2016). An improved Ant Colony Optimization (ACO) technique for estimation of flow functions (kr and Pc) from core-flood experiments. Journal of Natural Gas Science and Engineering, Vol. 33, pp. 624–633.
[228] Yong, H., Kang, L., Xiangzhou, Z., Lijun, S., Ngaic, E. W., and Mei, L. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, Vol. 36, pp. 534–551.
[229] Yun, P., Zhongli, D., Shenyong, Z. and Rong, P. (2011). Bayesian Network revision with Probabilistic Constraints. Intl. Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 20, No. 03, pp. 317-337. (doi: 10.1142/S021848851250016X).
[230] Yun-Sheng, Y., Han-Chieh, C., Ruay-Shiung, C. and Athanasios,, V. (2011). Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Mathematical and Computer Modeling. Vol. 53, Issues 11–12. Pp. 2238–2250.
[231] Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, Part I: 8, 199-249; Part II: 8, 301-357; Part III: 9, 43-80.
[232] Zadeh, L. A. (1965). Fuzzy Sets. Information and Control 8: 338–358.doi:10.1016/S0019-9958 (65)90241-X.
[233] Zadeh, L. A. (1994), “Fuzzy Logic, Neural Networks and Soft Computing”, Communication of the ACM, 37(3), pp. 77-84.
[234] Zadeh, L. A. (1973). "Outline of a new approach to the analysis of complex systems and decision processes". IEEE Trans. Systems, Man & Cybernetics. Vol. 3: 28–44.
[235] Zadeh, L. A. (1974). "Fuzzy logic and its application to approximate reasoning". In: Information Processing 74, Proc. IFIP Congr. (3), pp. 591–594.
[236] Zhi-jun, L., Qian, X. and Jian-guo, Y. (2011). Application of Genetic Algorithm-Support Vector Machine for Prediction of Spinning Quality. Proceedings of the World Congress on Engineering. Vol. 2, WCE, London, U.K.
[237] Zimmermann, H. J. (2001). Fuzzy Set Theory and Its Applications, Fourth Edition. Springer Science + Business Media, LLC, ISBN 978-94-010-3870-6 ISBN 978-94-010-0646-0 (eBook) DOI 10.1007/978-94-010-0646.
[238] Zomaya, A.Y. and Yee, H. T. (2001). Observations on using genetic algorithms for dynamic load-balancing, IEEE Transactions on Parallel and Distributed Systems, vol. 12, pp. 899-997.
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    Philip O. Omolaye, Joseph M. Mom, Gabriel A. Igwue. (2017). A Holistic Review of Soft Computing Techniques. Applied and Computational Mathematics, 6(2), 93-110. https://doi.org/10.11648/j.acm.20170602.15

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    Philip O. Omolaye; Joseph M. Mom; Gabriel A. Igwue. A Holistic Review of Soft Computing Techniques. Appl. Comput. Math. 2017, 6(2), 93-110. doi: 10.11648/j.acm.20170602.15

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    Philip O. Omolaye, Joseph M. Mom, Gabriel A. Igwue. A Holistic Review of Soft Computing Techniques. Appl Comput Math. 2017;6(2):93-110. doi: 10.11648/j.acm.20170602.15

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  • @article{10.11648/j.acm.20170602.15,
      author = {Philip O. Omolaye and Joseph M. Mom and Gabriel A. Igwue},
      title = {A Holistic Review of Soft Computing Techniques},
      journal = {Applied and Computational Mathematics},
      volume = {6},
      number = {2},
      pages = {93-110},
      doi = {10.11648/j.acm.20170602.15},
      url = {https://doi.org/10.11648/j.acm.20170602.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20170602.15},
      abstract = {Due to notable technological convergence that brought about exponential growth in computer world, Soft Computing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.},
     year = {2017}
    }
    

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    T1  - A Holistic Review of Soft Computing Techniques
    AU  - Philip O. Omolaye
    AU  - Joseph M. Mom
    AU  - Gabriel A. Igwue
    Y1  - 2017/04/10
    PY  - 2017
    N1  - https://doi.org/10.11648/j.acm.20170602.15
    DO  - 10.11648/j.acm.20170602.15
    T2  - Applied and Computational Mathematics
    JF  - Applied and Computational Mathematics
    JO  - Applied and Computational Mathematics
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    EP  - 110
    PB  - Science Publishing Group
    SN  - 2328-5613
    UR  - https://doi.org/10.11648/j.acm.20170602.15
    AB  - Due to notable technological convergence that brought about exponential growth in computer world, Soft Computing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria

  • Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria

  • Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria

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