IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 2    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 2,February 2012
A Hybrid Metaheuristic Algorithm for Classification using Micro array Data
Full Text(PDF, )  PP.520-528  
Author(s)
Mrs.Aruchamy Rajini, Dr. (Mrs.) Vasantha kalayani David
KEYWORDS
ACO, FNT, FA, Metaheuristic, Nature-inspired
ABSTRACT
A metaheuristic algorithms provide effective methods to solve complex problems using finite sequence of instructions. It can be defined as an iterative search process that efficiently performs the exploration and exploitation in the solution space aiming to efficiently find near optimal solutions. This iterative process has adopted various natural intelligences and aspirations. In this work, to find optimal solutions for microarray data, nature-inspired metaheuristic algorithms were adapted. A Flexible Neural Tree (FNT) model for microarray data is created using nature-inspired algorithms. The structure of FNT is created using the Ant Colony Optimization (ACO) and the parameters encoded in the neural tree are optimized by Firefly Algorithm (FA). FA is used to produce near optimal solutions and hence it is superior to the existing metaheuristic algorithm. Experimental results were analyzed in terms of accuracy and error rate to converge to the optimum. The proposed model is compared with other models for evaluating its performance to find the appropriate model.
References
[1] E. Elbeltag, T. Hegazy and D. Griersona, ‚Modified Shuffled Frog- Leaping Optimisation Algorithm: Applications to Project Management‛, Structure and Infrastructure Engineering, vol. 3, no. 1, 2007, pp. 53 – 60.

[2] E. Emad, H. Tarek, and G. Donald, ‚Comparison among Five Evolutionary-based Optimisation Algorithms‛, Advanced Engineering Informatics, vol.19, 2005, pp. 43-53.

[3] X.S. Yang, ‚A Discrete Firefly Meta-heuristic with Local Search for Make span Minimisation in Permutation Flow Shop Scheduling Problems‛, International Journal of Industrial Engineering Computations, vol. 1, 2010, pp. 1–10.

[4] X.S. Yang, ‚Firefly Algorithms for Multimodal Optimisation‛,Stochastic Algorithms: Foundations and Applications, SAGA 2009,Lecture Notes in Computer Sciences, vol. 5792, 2009, pp. 169-178.

[5] S. Lukasik and S. Zak, ‚Firefly Algorithm for Continuous Constrained Optimisation Tasks‛, Systems Research Institute, Polish Academy of Sciences, 2010, pp. 1–10.

[6] D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim and M.Zaidi, ‚The Bees Algorithm. Technical Note‛. ManufacturingEngineering Centre, Cardiff University, UK, 2005.

[7] D.T. Pham, Ghanbarzadeh A., Koc E., Otri S., Rahim S., and M.Zaidi, ‚The Bees Algorithm - A Novel Tool for Complex Optimisation Problems", Proceedings of IPROMS 2006 Conference, 2006, pp. 454-461.

[8] K.S. Lee and Z.W. Geem, ‚A New Meta-heuristic Algorithm for Continues Engineering Optimisation: Harmony Search Theory and Practice‛, Comput: Meth. Appl. Mech. Eng., vol. 194, 2004, pp.3902–3933.

[9] P. Muller and D.R. Insua, "Issues in Bayesian Analysis of Neural Network Models", Neural Computation, vol. 10, 1995, pp. 571–592.

[10] M. Dorigo, V. Maniezzo and A. Colorni, ‚Ant System: Optimisation by a Colony of Cooperating Agents‛, IEEE Transactions on Systems, Man, and Cybernetics Part B, vol. 26, numéro 1, 1996, pp. 29-41.

[11] J.Y. Jeon, J.H. Kim, and K. Koh ‚Experimental EvolutionaryProgramming-based High-precision Control,‛ IEEE Control Sys. Tech., vol. 17, 1997, pp. 66-74.

[12] R. Storn, ‚System Design by Constraint Adaptation and Differential Evolution", IEEE Trans. on Evolutionary Computation, vol. 3, no. 1,1999, pp. 22-34.

[13] M. Clerc, and J. Kennedy, ‚The Particle SwarmExplosion, Stability, and Convergence in a Multidimensional Complex Space‛, IEEETransactions on Evolutionary Computation, vol. 6, 2002, pp.58-73.

[14] Lokketangen, A. K. Jornsten and S. Storoy ‚Tabu Search within a Pivot and Complement Framework‛, International Transactions in Operations Research, vol. 1, no. 3, 1994, pp. 305-316.

[15] V. Granville, M. Krivanek and J.P. Rasson, ‚Simulated Annealing: a Proof of Convergence‛, Pattern Analysis and Machine Intelligence,IEEE Transactions, vol. 16, Issue 6, 1994, pp. 652 – 656.

[16] N.Chai-ead,P.Aungkulanon,and P.Luangpaiboon,‛Bees and Firefly Algorithms For Noisy Non-Linear Optimization Problems‛IMECS 2011,Vol II,March16-18,2011,HongKong.

[17] Aruchamy Rajini, Dr. (Mrs)Vasantha Kalayani David,‛AComparative Performance Study on Hybrid Swarm Model for Micro array Data‛, International Journal of Computer Applications(0975-8887) , Vol 30 No.6, Sept 2011.

[18] Y. Chen, B. Yang, J. Dong, Nonlinear systems modelling via optimal design of neural trees, International Journal of Neural System 14 (2004) 125–138.

[19] Y. Chen, B. Yang, J. Dong, A. Abraham, Time series forecasting using flexible neural tree model, Information Sciences 174 (2005) 219–235.

[20] Y. Chen, B. Yang, A. Abraham, Flexible neural trees ensemble for stock index modeling, Neurocomputing 70 (2007) 697–703.

[21] Y. Chen, F. Chen, J.Y. Yang, Evolving MIMO flexible neural trees for nonlinear system identification, in: IC-AI, 2007, pp. 373–377.

[22] Y. Chen, A. Abraham, B. Yang, ‚Hybrid flexible neural tree based intrusion detection systems‛, International Journal of Intelligent Systems 22 (4) (2007) 337–352.

[23] Lizhi Peng,Bo Yang,Lei Zhang,Yuehui Chen,‛A Parallel evolving algorithm for flexible neural tree‛,Parallel Computing 37 (2011),653-666.

[24] Yuehui Chen, Bo Yang and Jiwen Dong, ‚Evolving Flexible Neural Networks using Ant Programming and PSO Algorithm‛, Springer –Verlag Berlin Heidelberg 2004.

[25] H. Zang, S. Zhang and K. Hapeshi, ‛A Review of NatureInspired Algorithms‛, Journal of Bionic Engineering, vol. 7, 2010, pp. 232–237

[26] Xin-She Yang, X.S., (2010) ‘Firefly Algorithm, Stochastic Test Functions and Design Optimisation’, Int. J. BioInspired Computation, Vol. 2, No. 2, pp.78-84.

[27] X.-s. Yang,‛Firefly Algorithm, Levy flights and global optimization‛,in: Research and development in Intelligent Systems XXVI (Eds M. Bramer, R. Ellis, M. Petridis), Springer London, pp.209-218(2010).

[28] Available on the UW CS ftp server, ftp ftp.cs.wics.edu, cd math-prog/cpo-dataset/machine-learn/WPBC/

Untitled Page