Radial basis function network based on time variant multi-objective particle swarm optimization for medical diseases diagnosis

Applied Soft Computing Journal

Volumn 11, Issue 1, 2011, Pages 1427-1438

  Qasem, Sultan Noman ^a   Shamsuddin, Siti Mariyam ^a  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Elitist non dominated sorting genetic algorithm; Hybrid learning; Multi objective particle swarm optimization; Particle swarm optimization; Radial basis function network; Time variant multi objective particle swarm optimization

Indexed keywords

ELITIST NON-DOMINATED SORTING GENETIC ALGORITHMS; HYBRID LEARNING; MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION; PARTICLE SWARM; RADIAL BASIS FUNCTIONS; TIME VARIANT;

ATTITUDE CONTROL; DIAGNOSIS; GENETIC ALGORITHMS; LEARNING ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); SCREENING; SORTING;

RADIAL BASIS FUNCTION NETWORKS;

EID: 77957907005     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2010.04.014     Document Type: Article

References (42)

1. D. Broomhead, and D. Lowe Multivariable functional interpolation and adaptive networks Complex Systems 2 1988 321 355
2. J.A. Leonard, and M.A. Kramer Radial basis function networks for classifying process faults Control Systems Magazine 11 3 1991 31 38
3. X. Yao Evolving artificial neural networks Proceedings IEEE 87 9 1999 1423 1447
4. J. Fieldsend, and S. Singh Pareto evolutionary neural networks IEEE Transaction Neural Networks 16 2 2005 338 354
5. C.A. Coello Coello An updated survey of GA-based multi-objective optimization techniques ACM Computing Surveys 32 2 2000 109 143
6. C.A. Ceollo Coello, D.A. Van Veldhuizen, and G.B. Lamont Evolutionary Algorithms for Multi-Objective Problems 2002 Kluwer New York
7. N. Garcia-Perdrajas, E. Sanz-tapia, D. Ortiz-Boyer, and C. Hervas Introducing multi-objective optimization in cooperative coevolution of neural networks Proc. 6th Int. Work-Conference. Artificial Natural Neural Network 2001 645 652
8. N. Garcia-Perdrajas, C. Hervas, and J. Munoz-Perez Multi-objective cooperative coevolution of artificial neural networks Neural Networks 15 10 2002 1259 1278
9. G. Liu, and V. Kadirkamanathan Multi-objective criteria for neural networks structure selection and identification of nonlinear systems using genetic algorithms Proceedings Institute of Electrical Engineering: Control Theory and Applications 146 5 1999 373 382
10. E. de Lacerda, A. de Carvallho, and T. Ludermir Evolutionary optimization of RBF networks In Proc. 6th Braz. Symp. Neural Netw. 2000 219 224
11. J. Gonzalez, I. Rojas, J. Ortega, H. Pomares, F. Fernandez, and A. Diaz Multi-objective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation IEEE Transactions on Neural Network 14 6 2003 1478 1495
12. H.A. Abbass, and R. Sarker Simultaneous evolution of architectures and connection weights in ANNs Proc. ANNES Conf. 2001 16 21
13. H.A. Abbass A memetic pareto evolutionary approach to artificial neural networks Australian Joint Conference on Artificial Intelligence 2001 1 12
14. H.A. Abbass An evolutionary artificial neural networks approach for breast cancer diagnosis Artificial Intelligence in Medicine 25 3 2002 265 281
15. H.A. Abbass Speed up backpropagation using multi-objective evolutionary algorithms Neural Computation 15 11 2003 2705 2726
16. H.A. Abbass Pareto neuro-evolution: constructing ensemble of neural networks using multi-objective optimization Proc. CEC, vol. 3 2003 2074 2080
17. Y. Jin, T. Okabe, and B. Sendhoff Neural network regularization and ensembling using multi-objective evolutionary algorithms Proc. Cong. Evol. Comput. 2004 1 8
18. Y. Jin, and B. Sendhoff Alleviating catastrophic forgetting via multi-objective learning Proc. Int. Joint Conference Neural Netw. 2006 3335 3342
19. L. Graning, Y. Jin, and B. Sendhoff Generalization improvement in multi-objective learning Proc. Int. Joint Conf. Neural Netw. 2006 4839 4846
20. Y. Jin, B. Sendhoff, and E. Korner Evolutionary Multi-Objective Optimization for Simultaneous Generation of Signal-Type and Symbol-Type Representations vol. 3410 2005 Springer-Verlag Berlin, Germany pp. 752-766
21. I. Kokshenev, and A.P. Braga A multi-objective approach to RBF network learning Neurocomputing 71 2008 203 1209
22. G.G. Yen Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design 16 2006 Springer-Verlag pp. 221-239
23. N. Kondo, T. Hatanaka, and K. Uosaki Pattern classification by evolutionary RBF networks ensemble based on multi-objective optimization International Joint Conference on Neural Networks 2006 2919 2925 (Pubitemid 351369709)
24. V. Lefort, C. Knibbe, G. Beslon, and J. Favrel Simultaneous Optimization of Weights and Structure of an RBF Neural Network vol. 3871 2006 Springer-Verlag, LNCS pp. 49-60
25. J. Gonzalez, I.P.H. Rojas, and J. Ortega RBF Neural Networks, Multi-objective Optimization and Time Series Forecasting vol. 2084 2001 Springer-Verlag, LNCS pp. 498-505
26. N. Kondo, T. Hatanaka, and K. Uosaki Nonlinear dynamic system identification based on multi-objectively selected RBF networks Proceedings of the IEEE Symposium on Computational Intelligence in Multicriteria Decision Making 2007 112 127
27. P.M. Ferreira, A.E. Ruano, and C.M. Fonseca Evolutionary multi-objective design of radial basis function networks for greenhouse environmental control Proc. of the 16th IFAC World Congress 2005
28. Y. Bai, and L. Zhang Genetic algorithm based self-growing training for RBF neural networks Proceedings of International Joint Conference on Neural Networks 2002 840 845
29. Z. Wenbo, H. De-Shuang, and Y. Ge The structure optimization of radial basis probabilistic neural networks based on genetic algorithms Proceedings of International Joint Conference on Neural Networks 2002 1086 1091
30. J. Kennedy, and R.C. Eberhart Particle Swarm Optimization Proceedings of IEEE International Conference on Neural Networks, IV, Piscataway 1995 1942 1948
31. Y. Shi, and R.C. Eberhart A modified particle swarm optimizer IEEE Conference on Evolutionary Computation 1998 69 73
32. C.A.C. Coello, and M.S. Lechuga MOPSO: a proposal for multiple objective particle swarm optimization Proceedings of the 2002 Congress on Evolutionary Computation part of the 2002 IEEE World Congress on Computational Intelligence IEEE Press, Hawaii 2002 1051 1056
33. A. Ratnaweera, S.K. Halgamuge, and H.C. Watson Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients IEEE Transactions on Evolutionary Computation 8 3 2004 240 255
34. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, and A. Fast Elitist Multi-objective Genetic Algorithm: NSGA-II IEEE Transactions on Evolutionary Computation 6 2 2002 182 197
35. C. Coello, G. Pulido, and M. Salazar Handling multi-objectives with particle swarm optimization IEEE Transactions on Evolutionary Computation 8 2004 256 279
36. P.K. Tripathi, S. Bandyopadhyay, and S.K. Pal Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients International Journal of Information Sciences 177 3 2007 5033 5049
37. C.R. Raquel, and P.C. Naval An effective use of crowding distance in multiobjective particle swarm optimization Proceedings of the 2005 Conference on Genetic and Evolutionary Computation 2005 257 264
38. M. Shahin, M. Jaksa, and H. Maier Application of Neural Networks in Foundation Engineering Theme Paper to the International e-Conference on Modern Trends in Foundation Engineering: Geotechnical Challenges and Solutions, Theme No. 5: Numerical Modelling and Analysis Chennai, India 2004
39. A. Asuncion, and D. Newman UCI machine learning repository 2007 URL (http://www.ics.uci.edu/∼mlearn/MLRepository.html )
40. C. Goh, E. Teoh, and K.C. Tan Hybrid multiobjective evolutionary design for artificial neural networks IEEE Transactions on Neural Networks 19 9 2008 1531 1548
41. Y. Jin, and B. Sendhoff Pareto based approach to machine learning: an overview and case studies IEEE Transactions on Systems, Man, and Cybernetics, Part C 38 3 2008 397 415
42. O.L. Mangasarian, and W.H. Wolberg Cancer diagnosis via linear programming SIAM News 23 5 1990 1 18