Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology

Evolutionary Intelligence

Volumn 3, Issue 3, 2010, Pages 187-199

  Cruz Ramirez M ^a   Sanchez Monedero J ^a   Fernandez Navarro F ^a   Fernandez J C ^a   Hervas Martinez C ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Differential evolution; Memetic algorithms; Multiclassification; Multiobjective; Neural networks; Predictive microbiology

Indexed keywords

DIFFERENTIAL EVOLUTION; MEMETIC ALGORITHMS; MULTI OBJECTIVE; MULTI-CLASSIFICATION; PREDICTIVE MICRO-BIOLOGY;

BACKPROPAGATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); EVOLUTIONARY ALGORITHMS; MICROORGANISMS; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE;

NEURAL NETWORKS;

EID: 78650172837     PISSN: 18645909     EISSN: 18645917     Source Type: Journal    
DOI: 10.1007/s12065-010-0045-9     Document Type: Article

References (49)

1. Abbass HA (2001) A memetic pareto evolutionary approach to artificial neural networks. In: Brooks M, Corbet D, Stumptner M (eds) AI2001. LNAI 2256, Springer, pp 1-12.
2. Abbass HA, Sarker R, Newton C (2001) Pde: a pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 congress on evolutionary computation, vol 2. Seoul, South Korea.
3. Baranyi J, Roberts T (1995) Mathematics of predictive food microbiology. Int J Food Microbiol 26: 199-218.
4. Baranyi J, Ross T, McMeekin TA, Roberts TA (1996) Effects of parameterization on the performance of empirical models used in predictive microbiology. Int J Food Microbiol 13: 83-91.
5. Ben-David A (2007) A lot of randomness is hiding in accuracy. Eng Appl Artif Intell 20(7): 875-885.
6. Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, Oxford.
7. Buchanan RLB (1993) Expansion of response surface analysis for the growth of Escherichia coli O157: H7 to include sodium nitrite as a variable. Int J Food Microbiol 23: 317-332.
8. Coello Coello CA, Lamont GB, van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, vol XXII, 2nd edn. Springer, Berlin.
9. Davey K (1991) Applicability of the Davey, (linear Arrhenius) predictive model to the lag phase of microbial growth. J Appl Bacteriol 70: 253-257.
10. Deb K, Pratab A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga2. IEEE Trans Evol Comput 6(2): 182-197.
11. Fernández JC, Hervás C, Martínez FJ, Gutiérrez PA, Cruz M (2009) Memetic pareto differential evolution for designing artificial neural networks in multiclassification problems using cross-entropy versus sensitivity. In: Hybrid artificial intelligence systems, vol 5572. Springer, Berlin, pp 433-441.
12. Fernández JC, Martínez FJ, Hervás C, Gutiérrez PA (2010) Sensitivity versus accuracy in multi-class problems using memetic pareto evolutionary neural networks. IEEE Trans Neural Netw 21(5): 750-770.
13. Fernández-Navarro F, Valero A, Hervás-Martínez C, Gutiérrez PA, García-Gimeno RM, Zurera-Cosano G (2010) Development of a multi-classification neural network model to determine the microbial growth/no growth interface. Int J Food Microbiol 141: 203-212.
14. García D, Ramos AJ, Sanchis V, Marin S (2009) Predicting mycotoxins in foods: a review. Food Microbiol 26: 757-769.
15. Garcia-Gimeno RM, Hervas-Martinez C, Barco-Alcala E, Zurera-Cosano G, Sanz-Tapia E (2003) An artificial neural network approach to Escherichia coli O157: H7 growth estimation. J Food Sci 68(2): 639-645.
16. Garcia-Gimeno RM, Hervas-Martinez C, Rodriguez-Perez R, Zurera-Cosano G (2005) Modelling the growth of Leuconostoc mesenteroides by artificial neural networks. Int J Food Microbiol 105(3): 317-332.
17. Garcia-Gimeno RM, Hervas-Martinez C, Sanz-Tapia E, Zurera-Cosano G (2002) Estimation of microbial growth parameters by means of artificial neural networks. Food Sci Technol Int 8(2): 73-80.
18. Garcia-Gimeno RM, Hervas-Martinez C, de Siloniz MI (2002) Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food. Int J Food Microbiol 72(1-2): 19-30.
19. Gelenbe E, Hussain KF (2002) Learning in the multiple class random neural network. IEEE Trans Neural Netw 13(6): 1257-1267.
20. Hajmeer M, Basheer I (2002) A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. J Microbiol Methods 51: 217-226.
21. Hajmeer M, Basheer I, Najjar Y (1997) Computational neural networks for predictive microbiology: II. Application to microbe growth. Int J Food Microbiol 34: 51-66.
22. Hajmeer MN, Basheer IA (2003) A hybrid bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface. Int J Food Microbiol 82: 233-243.
23. Hajmeer MN, Basheer IA, Cliver DO (2006) Reliability-based estimation of the survival of listeria monocytogenes in chorizos. J Sci Food Agric 86(14): 2337-2344.
24. Hervas C, Zurera G, Garcia RM, Martinez JA (2001) Optimization of computational neural network for its application in the prediction of microbial growth in foods. Food Sci Technol Int 7(2): 159-163.
25. Hervas-Martinez C, Garcia-Gimeno RM, Martinez-Estudillo AC, Martinez-Estudillo FJ, Zurera-Cosano G (2006) Improving microbial growth prediction by product unit neural networks. J Food Sci 71(2): M31-M38.
26. Hudson J (1992) Construction of and comparison between response surface analysis for Aeromonas hydrophila ATCC 7966 and food isolate under aerobic conditions. J Food Prot 55: 968-972.
27. Igel C, Hüsken M (2000) Improving the rprop learning algorithm. In: Proceedings of the second international ICSC symposium on neural computation (NC 2000). ICSC Academic Press, pp 115-121.
28. Igel C, Hüsken M (2003) Empirical evaluation of the improved rprop learning algorithms. Neurocomputing 50(6): 105-123.
29. Jin Y, Sendhoff B (2008) Pareto-based multiobjective machine learning: an overview and case studies. IEEE Trans Syst Man Cybern C Appl Rev 38(3): 397-415.
30. Koutsoumanis K, Taoukis P, Nychas G (2005) Development of a safety mon itoring and assurance system for chilled food products. Int J Food Microbiol 100(1-3): 253-260.
31. Le Marc Y, Pin C, Baranyi J (2002) Methods to determine the growth domain in a multidimensional environmental space. Int J Food Microbiol 100(1-3): 312.
32. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. U. C. Berkeley Press, pp 281-297.
33. Miller R (1996) Beyond ANOVA, basics of app. statistics. Chapman & Hall, London.
34. Moscato P, Cotta C (2003) A gentle introduction to memetic algorithms. In: Handbook of Metaheuristics. International series in operations research and management science, vol 57. Springer, New York, pp 105-144.
35. Natarajan BK (1991) Machine learning: a theoretical approach. Morgan Kaufmann, San Mateo. ISBN: 1558601481.
36. Pitt R (1992) A descriptive model of mold growth and aflatoxin formation as affected by environmental conditions. J Food Prot 56: 139-146.
37. Presser K, Ross T, Ratkowsky D (1998) Modelling the growth limits (growth/no growth interface) of Escherichia coli as a function of temperature, pH, lactic acid concentration and water activity. Appl Environ Microbiol 64(5): 1773-1779.
38. Price D, Knerr S, Personnaz L, Dreyfus G (1995) Pairwise neural network classifiers with probabilistic outputs. In: Tesauro G, Touretzky DS, Leen TK (eds) Advances in neural information processing systems 7 (NIPS Conference, Denver, Colorado, USA, 1994). MIT Press, Cambridge, pp 1109-1116.
39. Ratkowsky DA, Ross T (1995) Modeling the bacterial no growth interface. Lett Appl Microbiol 20: 29-33.
40. Richard D, David ER (1989) Product units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput 1(1): 133-142.
41. Soriano JM, Font G, Moltó JC, manes J (2002) Enterotoxigenic staphylococci and their toxins in restaurant foods. Trends Food Sci Technol 13: 60-67.
42. Storn R (2008) Differential evolution research-trends and open questions. In: Advances in differential evolution. Studies in computational intelligence, vol 143, pp 1-31. doi: 10. 1007/978-3-540-68830-3.
43. Valero A, Hervás C, García-Gimeno RM, Zurera G (2007) Product unit neural network models for predicting the growth limits of Listeria monocytogenes. Food Microbiol 24(5): 452-464.
44. Valero A, Hervás C, García-Gimeno RM, Zurera G (2007) Searching for new mathematical growth model approaches for Listeria monocytogenes. J Food Sci 72(1): M16-M25.
45. Valero A, Pérez-Rodríguez F, Carrasco E, Fuentes-Alventosa JM, Garcfa-Gimeno RM, Zurera G (2009) Modelling the growth boundaries of Staphylococcus aureus: effect of temperature, pH and water activity. Int J Food Microbiol 133: 186-194.
46. Vapnik V (1998) Statistical learning theory. Wiley, New York.
47. Yu C, Davidson VJ, Yang SX (2006) A neural network approach to predict survival/death and growth/no-growth interfaces for Escherichia coli O157: H7. Food Microbiol 23(6): 552560.
48. Zaika LL, Moulden E, Weimer L, Phillips JG, Buchanan RL (1994) Model for the combined effects of temperature, initial pH, sodium chloride and sodium nitrite concentrations on anaerobic growth of Shigella flexneri. Int J Food Microbiol 23: 345-358.
49. Zhang GP (2000) Neural networks for classification: a survey. IEEE Trans Syst Man Cybern C Appl Rev 30(4): 451-462.