Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology

Applied Soft Computing Journal

Volumn 11, Issue 1, 2011, Pages 534-550

  Fernandez J C ^a   Hervas C ^a   Martinez Estudillo F J ^b   Gutierrez P A ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b UNIVERSIDAD LOYOLA ANDALUCÍA   (Spain)

Author keywords

Accuracy; Classification; Memetic algorithms; Multiobjective; Neural networks; Predictive microbiology; Sensitivity

Indexed keywords

ACCURACY; CLASSIFICATION; MEMETIC ALGORITHMS; MULTI OBJECTIVE; PREDICTIVE MICRO-BIOLOGY; SENSITIVITY;

BACKPROPAGATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); ESCHERICHIA COLI; EVOLUTIONARY ALGORITHMS; LEARNING ALGORITHMS; MICROORGANISMS; MULTIOBJECTIVE OPTIMIZATION;

NEURAL NETWORKS;

EID: 77957930634     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.12.013     Document Type: Article

References (75)

1. A. Yardimci Soft computing in medicine Applied Soft Computing 9 2009 1029 1043
2. D. Garcia, A.J. Ramos, V. Sanchis, and S. Marin Predicting mycotoxins in foods: a review Food Microbiology 26 2009 757 769
3. W. Wan, S. Mabu, K. Shimada, K. Hirasawa, and J. Hu Enhancing the generalization ability of neural networks through controlling the hidden layers Applied Soft Computing 9 1 2009 404 414
4. C.M. Bishop Neural Networks for Pattern Recognition 1995 Oxford University Press
5. S. Haykin Neural Networks: A Comprehensive Foundation 2nd edition 1998 Prentice Hall
6. S. Ari, and G. Saha In search of an optimization technique for artificial neural network to classify abnormal heart sounds Applied Soft Computing 9 1 2009 330 340
7. C.N. Gupta, R. Palaniappan, S. Swaminathan, and S.M. Krishnan Neural network classification of homomorphic segmented heart sounds Applied Soft Computing 7 1 2007 286 297
8. C.A. Coello, G.B. Lamont, and D.A.V. Veldhuizen Evolutionary Algorithms for Solving Multi-objective Problems 2007 Springer
9. A. Valero, F. Pérez-Rodríguez, E. Carrasco, J.M. Fuentes-Alventosa, R.M. García-Gimeno, and G. Zurera Modelling the growth boundaries of Staphylococcus aureus: effect of temperature, pH and water activity International Journal of Food Microbiology 133 2009 186 194
10. T Ross, P. Dalgaard, and S. Tienungoon Predictive modelling of the growth and survival of Listeria in fishery products International Journal of Food Microbiology 62 2000 231 245
11. T.A. McMeekin, and T. Ross Modeling applications Journal of Food Protection Suppl. 1996 1 88
12. D. Schaffner, and T.P. Labuza Summing it up: predictive microbiology Food Technology 51 1997 95 99
13. T.A. McMeekin, K. Presser, D.A. Ratkowsky, T. Ross, M. Salter, and S. Tienungoon Quantifying the hurdle concept by modelling the bacterial growth/no growth interface International Journal of Food Microbiology 55 2000 93 98
14. C. Genigeorgis, S. Martin, C.E. Franti, and H. Reimann Initiation of staphylococcal growth in laboratory media Applied Microbiology 21 1971 934 939
15. D.A. Ratkowsky, and T. Ross Modelling the bacterial growth/no growth interface Letters in Applied Microbiology 20 1995 29 33
16. R. Caruana, and A. Niculescu-Mizil Data mining in metric space: an empirical analysis of supervised learning performance criteria Proceedings of the 10th Int. Conf. Knowl. Disc. Data Mining 2004 69 78
17. T. Fawcett An introduction to ROC analysis Pattern Recognition Letters 27 2006 861 874
18. S. Gídlant Neural-network Learning and Expert Systems 1993 MIT Press
19. R. Parekh, J. Yang, and V. Honavar Constructive neural-network learning algorithms for pattern classification IEEE Transactions on Neural Networks 11 2000 436 450
20. R. Reed Pruning algorithms. A survey IEEE Transactions on Neural Networks 4 1993 740 747
21. T. Kondo Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control Applied Soft Computing 7 1 2007 189 202
22. A. Saxena, and A. Saad Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems Applied Soft Computing 7 1 2007 441 454
23. A.J. van Rooij, L.C. Jain, R.P. Johnson, Neural Networks Training Using Genetic Algorithms, World Scientific, Series in Machine Perception and Artificial Intelligence, 1996.
24. X. Yao, and Y. Liu A new evolutionary system for evolving artificial neural networks IEEE Transactions on Neural Networks 8 3 1997 694 713
25. X. Yao Evolving artificial neural networks Proceedings of the IEEE 1999 1423 1447
26. W. Yan, Z. Zhu, and R. Hu Hybrid genetic/BP algorithm and its application for radar target classification Proceedings of the 1997 IEEE National Aerospace and Electronics Conference NAECON 1997 IEEE Press 981 984
27. I.A. Basheer, and M.N. Hajmeer Artificial neural networks: fundamentals, computation, design and application Journal of Microbiological Methods 43 2000 3 31
28. M.N. Hajmeer, I.A. Basheer, J.L. Mardsden, and D.Y.C. Fung New approach for modeling generalized microbial growth curves using artificial neural networks Journal of Rapid Methods & Automation in Microbiology 8 4 2000 265 284
29. A.W. Schepers, J. Thibault, and C. Lacroix Comparison of simple neural network and nonlinear regression models for descriptive modeling of Lactobacillus helveticus growth in pH-controlled batch cultures Enzyme and Microbial Technology 26 2000 431 445
30. S. Jeyamkondan, D.S. Jayas, and R.A. Holley Microbial growth modelling with artificial neural networks International Journal of Food Microbiology 64 2001 343 354
31. W. Lou, and S. Nakai Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH, and water activity Food Research International 34 7 2001 573 579
32. R.M. García-Gimeno, C. Hervas-Martinez, E. Barco-Alcala, G. Zurera-Cosano, and E. Sanz-Tapia An artificial neural network approach to Escherichia coli O157:H7 growth estimation Journal of Food Science 68 2003 639 645
33. R.M. García-Gimeno, C. Hervas-Martinez, R. Rodriguez-Perez, and G. Zurera-Cosano Modelling the growth of Leuconostoc mesenteroides by artificial neural networks International Journal of Food Microbiology 2005 317 332
34. M. Hajmeer, and I. Basheer A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data Journal of Microbiological Methods 51 2002 217 226
35. E.A. El-Sebakhy, and K.A. Faisal Bacterial growth classification with support vector machines: a comparative study The 2007 International Conference on Machine Learning; Models, Technologies and Applications (MLMTA'07) 2007 CSREA Press 90 98
36. E.A. El-Sebakhy, I. Raharja, S. Adem, and Y. Khaeruzzaman Neuro-fuzzy systems modeling tools for bacterial growth EEE/ACS International Conference on Computer Systems and Applications 2007 374 380
37. M.N. Hajmeer, and I.A. Basheer Comparison of logistic regression and neural network-based classifiers for bacterial growth Food Microbiology 20 2003 43 55
38. A. Valero, C. Hervás, R.M. García-Gimeno, and G. Zurera Product unit neural networks models for predicting the growth limits of Listeria monocytogenes Food Microbiology 24 2007 452 464
39. K. Deb Multi-objective Optimization using Evolutionary Algorithms 2004 John Wiley & Sons, Ltd.
40. Y. Jin, and B. Sendhoff Pareto-based multiobjective machine learning: an overview and case studies IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 38 3 2008 397 415
41. H.A. Abbass Speeding up backpropagation using multiobjective evolutionary algorithms Neural Computation 15 2003 2705 2726
42. V.V.R. Silva, P.J. Fleming, J. Sugimoto, and R. Yokoyama Multiobjective optimization using variable complexity modelling for control system design Applied Soft Computing 8 1 2008 392 401
43. F. Pettersson, N. Chakraborti, and H. Saxén A genetic algorithms based multi-objective neural net applied to noisy blast furnace data Applied Soft Computing 7 1 2007 387 397
44. C. Cotta, and P. Moscato A gentle introduction to memetic algorithms F. Glover, G.A. Kochenberger, Handbook on Metaheuristics 2001 Kluwer Academics
45. Y. Jin, B. Sendholf, and E. Körner Simultaneous generation of accurate and interpretable neural network classifiers Studies in Computational Intelligence (SCI) 6 2006 291 312
46. S. Wiegand, and C. Igel Evolutionary multi-objective optimization of neural networks for face detection International Journal of Computation Intelligence and Applications 4 3 2004 237 253
47. S. Roth, A. Gepperth, and C. Igel Multi-objective neural network optimization for visual object detection Multi-Objective Machine Learning 2006 629 655
48. F. Provost, and T. Fawcett Robust classification system for imprecise environments Proceedings of the Fifteenth National Conference on Artificial Intelligence 1998 706 713
49. K. Deb, A. Pratab, S. Agarwal, and T. Meyarivan A fast and elitist multiobjective genetic algorithm: NSGA2 IEEE Transactions on Evolutionary Computation 6 2 2002 182 197
50. C. Igel, and M. Hüsken Improving the Rprop learning algorithm Proceedings of the Second International ICSC Symposium on Neural Computation (NC 2000) ICSC 2000 Academic Press 115 121
51. P.J. Angeline, G.M. Saunders, and J.B. Pollack An evolutionary algorithm that constructs recurrent neural networks IEEE Transactions on Neural Networks 5 1 1994 54 65
52. C. Hervás, and F.J. Martínez-Estudillo Logistic regression using covariates obtained by product-unit neural network models Pattern Recognition 40 2007 52 64
53. A. Martinez-Estudillo, F. Martinez-Estudillo, C. Hervas-Martinez, and N. Garcia-Pedrajas Evolutionary product unit based neural networks for regression Neural Networks 19 4 2006 477 486
54. F.J. Martínez-Estudillo, C. Hervás-Martínez, P.A. Gutiérrez, and A.C. Martínez-Estudillo Evolutionary product-unit neural networks classifiers Neurocomputing 72 1-3 2008 548 561
55. C.R. Houck, J.A. Joines, M.G. Kay, and J.R. Wilson Empirical investigation of the benefits of partial Lamarckianism Evolutionary Computation 5 1997 31 60
56. Y.S. Ong, M.H. Lim, N. Zhu, and K.W. Wong Classification of adaptive memetic algorithms: a comparative study IEEE Transactions on Systems, Man and Cybernetics Part B 36 1 2006 141 152
57. J.E. Smith Coevolving memetic algorithms: a review and progress report IEEE Transactions on Systems, Man and Cybernetics Part B 37 1 2007 6 17
58. Y Jin, T. Okabe, and B. Sendhoff Neural network regularization and ensembling using multi-objective evolutionary algorithms Proceedings of 2004 Congress on Evolutionary and Ensembling Using Multi-objective Evolutionary Algorithms 2004 1 8
59. H.A. Abbass An evolutionary artificial neural networks approach for breast cancer diagnosis Artificial Intelligence in Medicine 25 2002 265 281
60. A. Gepperth, and S. Roth Applications of multi-objective structure optimization Neurocomputing 69 2006 701 703
61. M. Riedmiller, and H. Braun A direct adaptive method for faster backpropagation learning: the RPROP algorithm, neural networks IEEE International Conference 1993 586 591
62. C. Igel, and M. Hüsken Empirical evaluation of the improved Rprop learning algorithms Neurocomputing 50 6 2003 105 123
63. A. Valero, C. Hervás, R.M. García-Gimeno, and G. Zurera Searching for new mathematical growth model approaches for Listeria monocytogenes Journal of Food Science 72 1 2007 16 25
64. M.A. Salter, D.A. Ratkowsky, T. Ross, and T.A. McMeekin Modelling the combined temperature and salt (NaCl) limits for growth of a pathogenic Escherichia coli strain using nonlinear logistic regression International Journal of Food Microbiology 61 2000 159 167
65. L.L. Zaika, E. Moulden, L. Weimer, J.G. Phillips, and R.L. Buchanan Model for the combined effects of temperature, initial pH, sodium chloride and sodium nitrite concentrations on anaerobic growth of Shigella flexneri International Journal of Food Microbiology 23 1994 345 358
66. A.P Bradley The use of the area under the ROC curve in the evaluation of machine learning algorithms Pattern Recognition 30 7 1997 1145 1159
67. N. Landwehr, M. Hall, and F. Eibe Logistic model trees Machine Learning 59 2005 161 205
68. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2005 Morgan Kaufmann
69. C.C. Chang, C.J. Lin, LIBSVM: a library for support vector machines, Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm, 2001.
70. C.A. Hwang, and M.L. Tamplin The influence of mayonnaise pH and storage temperature on the growth of Listeria monocytogenes in seafood salad International Journal of Food Microbiology 102 2005 277 285
71. E. Commission, Opinion of the scientific committee on veterinary measures relating to public health on Listeria monocytogenes, http://www.europa.eu.int/ comm/food/fs/sc/scv/out25, 1999.
72. J.M. Soriano, G. Font, J.C. Moltó, and J. Mañes Enterotoxigenic staphylococci and their toxins in restaurant foods Trends of Food Science and Technology 13 2002 60 67 (Pubitemid 35240917)
73. J. Demsar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
74. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics 11 1 1940 86 92
75. J.W. Tukey Exploratory Data Analysis 1977 Addison-Wesley