Abstract
In this paper, a Differential Evolution (DE) algorithm for solving multiobjective optimization problems to solve the problem of tuning Artificial Neural Network (ANN) parameters is presented. The multiobjective evolutionary used in this study is a Differential Evolution algorithm while ANN used is Three-Term Backpropagation network (TBP). The proposed algorithm, named (MODETBP) utilizes the advantages of multi objective differential evolution to design the network architecture in order to find an appropriate number of hidden nodes in the hidden layer along with the network error rate. For performance evaluation, indicators, such as accuracy, sensitivity, specificity and 10-fold cross validation are used to evaluate the outcome of the proposed method. The results show that our proposed method is viable in multi class pattern classification problems when compared with TBP Network Based on Elitist Multiobjective Genetic Algorithm (MOGATBP) and some other methods found in literature. In addition, the empirical analysis of the numerical results shows the efficiency of the proposed algorithm.
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References
Mineu, N.L., Ludermir, T.B., Almeida, L.M.: Topology optimization for artificial neural networks using differential evolution. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2010)
Ding, S., Su, C., Yu, J.: An optimizing BP neural network algorithm based on genetic algorithm. Artificial Intelligence Review 36, 153–162 (2011)
Zhang, C., Shao, H., Li, Y.: Particle swarm optimisation for evolving artificial neural network. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics (2000), pp. 2487–2490. IEEE (2000)
Ilonen, J., Kamarainen, J.-K., Lampinen, J.: Differential evolution training algorithm for feed-forward neural networks. Neural Processing Letters 17, 93–105 (2003)
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1, 32–49 (2011)
Goh, C.-K., Teoh, E.-J., Tan, K.C.: Hybrid multiobjective evolutionary design for artificial neural networks. IEEE Transactions on Neural Networks 19, 1531–1548 (2008)
Delgado, M., Cuéllar, M.P., Pegalajar, M.C.: Multiobjective hybrid optimization and training of recurrent neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38, 381–403 (2008)
Qasem, S.N., Shamsuddin, S.M.: Memetic elitist pareto differential evolution algorithm based radial basis function networks for classification problems. Applied Soft Computing 11, 5565–5581 (2011)
Qasem, S.N., Shamsuddin, S.M.: Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis. Applied Soft Computing 11, 1427–1438 (2011)
Fernandez Caballero, J.C., Martínez, F.J., Hervás, C., Gutiérrez, P.A.: Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks. IEEE Transactions on Neural Networks 21, 750–770 (2010)
Jin, Y., Okabe, T., Sendhoff, B.: Neural network regularization and ensembling using multi-objective evolutionary algorithms. In: Congress on Evolutionary Computation, CEC 2004, pp. 1–8. IEEE (2004)
Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 Congress on Evolutionary Computation, pp. 971–978. IEEE (2001)
Abbass, H.A., Sarker, R.: The Pareto differential evolution algorithm. International Journal on Artificial Intelligence Tools 11, 531–552 (2002)
Fieldsend, J.E., Singh, S.: Pareto evolutionary neural networks. IEEE Transactions on Neural Networks 16, 338–354 (2005)
Abbass, H.A., Sarker, R.: Simultaneous evolution of architectures and connection weights in ANNs. In: Proceedings of Artificial Neural Networks and Expert System Conference, pp. 16–21 (2001)
Abbass, H.A.: A memetic pareto evolutionary approach to artificial neural networks. In: Stumptner, M., Corbett, D.R., Brooks, M. (eds.) Canadian AI 2001. LNCS (LNAI), vol. 2256, pp. 1–12. Springer, Heidelberg (2001)
Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine 25, 265–281 (2002)
Liu, G., Kadirkamanathan, V.: Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms. IEE Proceedings-Control Theory and Applications 146, 373–382 (1999)
Cruz-Ramírez, M., Hervás-Martínez, C., Gutiérrez, P.A., Pérez-Ortiz, M., Briceño, J., de la Mata, M.: Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem. Soft Computing 17, 275–284 (2013)
Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Storn, R.: System design by constraint adaptation and differential evolution. IEEE Transactions on Evolutionary Computation 3, 22–34 (1999)
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10, 646–657 (2006)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12, 64–79 (2008)
Tsai, J.-T., Ho, W.-H., Chou, J.-H., Guo, C.-Y.: Optimal approximation of linear systems using Taguchi-sliding-based differential evolution algorithm. Applied Soft Computing 11, 2007–2016 (2011)
Babu, B., Jehan, M.M.L.: Differential evolution for multi-objective optimization. In: The 2003 Congress on Evolutionary Computation, CEC 2003, pp. 2696–2703. IEEE (2003)
Ali, M., Siarry, P., Pant, M.: An efficient differential evolution based algorithm for solving multi-objective optimization problems. European Journal of Operational Research 217, 404–416 (2012)
Alatas, B., Akin, E., Karci, A.: MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. Applied Soft Computing 8, 646–656 (2008)
Gong, W., Cai, Z.: A multiobjective differential evolution algorithm for constrained optimization. In: IEEE Congress on Evolutionary Computation, CEC 2008 (IEEE World Congress on Computational Intelligence), pp. 181–188. IEEE (2008)
Zweiri, Y., Whidborne, J., Seneviratne, L.: A three-term backpropagation algorithm. Neurocomputing 50, 305–318 (2003)
Ibrahim, A.O., Shamsuddin, S.M., Ahmad, N.B., Qasem, S.N.: Three-Term Backpropagation Network Based On Elitist Multiobjective Genetic Algorithm for Medical Diseases Diagnosis Classification. Life Science Journal 10 (2013)
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Ibrahim, A.O., Shamsuddin, S.M., Qasem, S.N. (2014). Multiobjective Differential Evolutionary Neural Network for Multi Class Pattern Classification. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_64
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DOI: https://doi.org/10.1007/978-3-319-07692-8_64
Publisher Name: Springer, Cham
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