Multi-objective hybrid evolutionary algorithms for radial basis function neural network design

Broomhead, 1988, Multivariable functional interpolation and adaptive networks, Complex Systems, 2, 321 Shen, 2011, Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm, Knowledge-Based Systems, 24, 378, 10.1016/j.knosys.2010.11.001 Bishop, 1995 Teixeira, 2000, Improving generalization of MLPs with multi-objective optimization, Neurocomputing, 35, 189, 10.1016/S0925-2312(00)00327-1 Abbass, 2003, Speed up backpropagation using multi-objective evolutionary algorithms, Neural Computation, 15, 2705, 10.1162/089976603322385126 Yao, 1999, Evolving artificial neural networks, Proceedings IEEE, 87, 1423, 10.1109/5.784219 Fieldsend, 2005, Pareto evolutionary neural networks, IEEE Transactions on Neural Networks, 16, 338, 10.1109/TNN.2004.841794 Coello, 2000, An updated survey of GA-based multi-objective optimization techniques, ACM Computer Survey, 32, 109, 10.1145/358923.358929 Coello, 2002 N. Garcı´a-Perdrajas, E. Sanz-tapia, D. Ortiz-Boyer, C. Hervás-Martı´neza, Introducing multi-objective optimization in cooperative coevolution of neural networks, in: Proceedings of 6th International Work-Conference of Artificial Natural Neural Network, 2001, pp. 645–652. Garcı´a-Perdrajas, 2002, Multi-objective cooperative coevolution of artificial neural networks, Neural Networks, 15, 1259, 10.1016/S0893-6080(02)00095-3 Liu, 1999, Multi-objective criteria for neural networks structure selection and identification of nonlinear systems using genetic algorithms, Proceedings of Institute of Electrical Engineering –Control Theory and Applications, 146, 373, 10.1049/ip-cta:19990501 H.A. Abbass, R. Sarker, Simultaneous evolution of architectures and connection weights in ANNs, in: Proceedings of Artificial Neural Networks and Expert System Conference, 2001, pp. 16–21. H.A. Abbass, A memetic Pareto evolutionary approach to artificial neural networks, in: Australian Joint Conference on Artificial Intelligence, 2001, pp. 1–12. Abbass, 2002, An evolutionary artificial neural networks approach for breast cancer diagnosis, Artificial Intelligence in Medicine, 25, 265, 10.1016/S0933-3657(02)00028-3 H.A. Abbass, Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization, in: IEEE Congress on Evolutionary Computation, vol. 3, 2003, pp. 2074–2080. Jin, 2004, Neural network regularization and ensembling using multi-objective evolutionary algorithms, IEEE Congress on Evolutionary Computation, 1 Y. Jin, B. Sendhoff, Alleviating catastrophic forgetting via multi-objective learning, in: Proceeding International Joint Conference Neural Network, 2006, pp. 3335–3342. L. Graning, Y. Jin, B. Sendhoff, Generalization improvement in multi-objective learning, in: Proceedings of the International Joint Conference on Neural Networks, 2006, pp. 4839–4846. Jin, 2005 Kokshenev, 2010, An efficient multi-objective learning algorithm for RBF neural network, Neurocomputing, 73, 2799, 10.1016/j.neucom.2010.06.022 Kokshenev, 2008, A multi-objective approach to RBF network learning, Neurocomputing, 71, 1203, 10.1016/j.neucom.2007.11.021 Yen, 2006, vol. 16 N. Kondo, T. Hatanaka, K. Uosaki, Pattern classification by evolutionary RBF networks ensemble based on multi-objective optimization, in: International Joint Conference on Neural Networks, 2006, pp. 2919–2925. Lefort, 2006, Simultaneous optimization of weights and structure of an RBF neural network, vol. 3871 González, 2001, RBF neural networks, multi-objective optimization and time series forecasting, vol. 2084 N. Kondo, T. Hatanaka, K. Uosaki, Non linear dynamic system identification based on multi-objectively selected RBF networks, in: Proceedings of the IEEE Symposium on Computational Intelligence in Multi-criteria Decision Making, 2007, pp. 112–127. P.M. Ferreira, A.E. Ruano, C.M. Fonseca, Evolutionary multi-objective design of Radial Basis Function Networks for greenhouse environmental control, in: Proceedings of the 16 the IFAC World Congress, 2005. E. Lacerda, A. Carvallho, T. Ludermir, Evolutionary optimization of RBF networks, in: Proceedings of the 6th Brazilian Symposium Neural Networks, 2000, pp. 219–224. González, 2003, Multi-objective evolutionary optimization of the size shape and position parameters of Radial Basis Function Networks for function approximation, IEEE Transactions on Neural Networks, 14, 1478, 10.1109/TNN.2003.820657 Qasem, 2011, Radial Basis Function Network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis, Applied Soft Computing, 11, 1427, 10.1016/j.asoc.2010.04.014 J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, in: IEEE Conference on Evolutionary Computation, 1998, pp. 69–73. Huang, 2011, A hybrid particle swarm optimization approach for clustering and classification of datasets, Knowledge-Based Systems, 24, 420, 10.1016/j.knosys.2010.12.003 Alatas, 2009, Multi-objective rule mining using a chaotic particle swarm optimization algorithm, Knowledge-Based Systems, 22, 455, 10.1016/j.knosys.2009.06.004 Holland, 1975 R. Storn, K. Price, Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical report TR-95-012, ICSI, 1995. Karaboga, 2004, A simple and global optimization algorithm for engineering problems: differential evolution algorithm, Turkish Journal of Electrical Engineering and Computer Sciences, 12, 53 Goldberg, 1989 S. Baluja, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning, Technical report CMU-CS-94-163, Computer Science Department, Carnegie Mellon University, 1994. Vakil-Baghmishah, 2004, Training RBF networks with selective backpropagation, Nerocomputing, 62, 39, 10.1016/j.neucom.2003.11.011 C.R. Raquel, P.C. Naval, An effective use of crowding distance in multi-objective particle swarm optimization, in: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, 2005, pp. 257–264. Clerc, 2002, The particle swarm – explosion, stability, and convergence in a multidimensional complex space, IEEE Transactions on Evolutionary Computation, 6, 58, 10.1109/4235.985692 Deb, 2002, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 182, 10.1109/4235.996017 Deb, 1995, Simulated binary crossover for continuous search space, Complex System, 9, 115 A. Asuncion, D. Newman, UCI Machine Learning Repository. <http://www.ics.uci.edu/~mlearn/MLRepository.html>, 2007. Hervás, 2008, Multilogistic regression by evolutionary neural network as a classification tool to discriminate highly overlapping signals: qualitative investigation of volatile organic compounds in polluted waters by using headspacemass spectrometric analysis, Chemometrics and Intelligent Laboratory Systems, 92, 179, 10.1016/j.chemolab.2008.03.005 Mangasarian, 1990, Cancer diagnosis via linear programming, SIAM News, 23, 1 Jin, 2008, Pareto-based multiobjective machine learning: an overview and case studies, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 38, 397, 10.1109/TSMCC.2008.919172 Chawla, 2002, SMOTE: synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321, 10.1613/jair.953 M.-L. Antonie, O.R. Zaiane, R.C. Holte, Learning to use a learned model: a two-stage approach to classification, in: Proceedings 6th International Conference of Data Mining, 2006, pp. 33–42. Goh, 2008, Hybrid multi-objective evolutionary design for artificial neural networks, IEEE Transactions on Neural Networks, 19, 1531, 10.1109/TNN.2008.2000444 Cai, 2010, A multi-objective simultaneous learning framework for clustering and classification, IEEE Transactions on Neural Networks, 21, 185, 10.1109/TNN.2009.2034741 Caballero, 2010, Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks, IEEE Transactions on Neural Networks, 21, 750, 10.1109/TNN.2010.2041468 Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, 7, 1