Role of Hybrid Evolutionary Algorithms in Pattern Recognition  
  Authors : Amarbir Singh; Palwinder Singh

 

The Pattern Recognition has attracted the attention of researchers in last few decades as a machine learning approach due to its wide spread application areas. The application area includes medicine, communications, automations, military intelligence, data mining, bioinformatics, document classification, speech recognition, business and many others. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, self-adaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., but in this review paper, we illustrate the various possibilities for hybridization of an evolutionary algorithm and their application in the field of Pattern Recognition.

 

Published In : IJCAT Journal Volume 3, Issue 3

Date of Publication : April 2016

Pages : 244-249

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Publication Link :Role of Hybrid Evolutionary Algorithms in Pattern Recognition

 

 

 

Amarbir Singh : Department of Computer Science, Guru Nanak Dev University Amritsar, Punjab 143001, India

Palwinder Singh : Department of Computer Science, Guru Nanak Dev University Amritsar, Punjab 143001, India

 

 

 

 

 

 

 

Evolutionary Algorithms, Fuzzy Logic, Hybrid Algorithms, Genetic Algorithms, Neural Networks

An exhaustive survey of different hybrid computing algorithms for pattern recognition is presented. This paper has presented various combining methods of neural-fuzzy-genetic networks for producing an improved performance on real-world classification problem, in particular pattern recognition. In case of noisy patterns, choice of statistical model is a good solution. Practical importance of structural model depends upon recognition of simple pattern primitives and their relationships representation description language. As evident from the scientific literature/databases, the use of hybrid evolutionary algorithms are getting very popular.

 

 

 

 

 

 

 

 

 

[1] Holland JH, Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI, 1975. [2] Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Stuttgart, Fromman-Holzboog, 1973. [3] Schwefel HP, Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie, Basel, Birkhaeuser, 1977. [4] Fogel LJ, Owens AJ, and Walsh MJ, Artificial Intelligence through Simulated Evolution, Wiley, USA, 1966. [5] Koza JR, Genetic Programming, MIT Press, Cambridge, MA, 1966. [6] G.Nagalakshmi, S.Jyothi,”A Survey on Pattern Recognition using Fuzzy Clustering Approaches”, IRJES, vol.2, pp.24. [7] Sung-Bae Cho, Fusion of neural networks with fuzzy logic and genetic algorithm, IOS Press, 2002. [8] Shahin Ara Begum and O.Mema Devi, “Fuzzy Algorithm for Pattern Recognition” Medical Diagnosis,Assam Univ. Journal,vol.7 No.II, 2011. [9] H.Ishibuchi, K. Kwon, and H. Tanaka, “A Learning algorithm of fuzzy Neural Networks with triangular fuzzy weights,” Syst., vol.71, pp.277-293, 1995. [10] Jayanta Kumar Basu, Debnath Bhattacharyya, “Tai Artificial Neural Network in Pattern Recognition”, International Journal of Software Engineering and its Applications, 2010. [11] Deep Malya Mukhopadhyay, Maricel O. Balitanas, Alisherov Farkhood , ”Genetic Algorithm: A Tutorial Review”, Vol.2, No.3, 2009. [12] Tan KC, Yu Q, Heng CM, and Lee TH (2003) Evolutionary computing for knowledge discovery in medical diagnosis, Artificial Intelligence in Medicine, 27(2), pp. 129–154. [13] Koza JR, Genetic Programming: On the Programming of Computers By Means of Natural Selection, MIT Press, Cambridge, MA, 1992. [14] Zmuda MA, Rizki MM, and Tamburino LA, “Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems”, Applied Soft Computing, 2(4), 2003, pp. 269–282. [15] Holland JH, Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI, 1975. [16] Fogel DB, System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling, Ginn & Co., Needham, MA, 1991. [17] Fogel DB, An introduction to simulated evolutionary optimization. IEEE Transaction on Neural Networks, 5(1), 1994, pp. 3–14. [18] Wang L, “A hybrid genetic algorithm-neural network strategy for simulation optimization”, Applied Mathematics and Computation, 170(2), 2003, pp. 1329–1343. [19] Shi XH, Liang YC, Lee HP, Lu C, and Wang LM, “An improved GA and a novel PSO-GA-based hybrid algorithm”, Information Processing Letters, 93(5), 2005, pp. 255–261. [20] Grimaldi EA, Grimacia F, Mussetta M, Pirinoli P, and Zich RE, “A new hybrid genetical – swarm algorithm for electromagnetic optimization”, In Proceedings of International Conference on Computational Electromagnetics and its Applications, Beijing, China, 2004, pp. 157–160. [21] Tseng LY and Liang SC, “A hybrid metaheuristic for the quadratic assignment problem”, Computational Optimization and Applications, 34(1), 2005, pp. 85– 113. [22] Aruldoss AVT and Ebenezer JA, “A modified hybrid EP-SQP approach for dynamic dispatch with valvepoint effect”, International Journal of Electrical Power and Energy Systems, 27(8), 2005, pp. 594–601. [23] Attaviriyanupap KH, Tanaka E, and Hasegawa J, “A hybrid EP and SQP for dynamic economic dispatch with nonsmooth incremental fuel cost function, IEEE Transaction on Power Systems, 17(2), 2002, pp. 411– 416. [24] Burke EK and Smith AJ , ” Hybrid evolutionary techniques for the maintenance scheduling problem”, IEEE Transactions on Power Systems, 1(1), 2000, pp. 122–128.