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.
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.