Neural networks applied in chemistry. II. Neuro-evolutionary techniques in process modeling and optimization

Industrial and Engineering Chemistry Research

Volumn 52, Issue 36, 2013, Pages 12673-12688

  Cartwright, Hugh ^a   Curteanu, Silvia ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b GHEORGHE ASACHI TECHNICAL UNIVERSITY OF IASI   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL DYNAMIC; IN-PROCESS; MODELING METHODOLOGY; NEURAL MODELING;

NEURAL NETWORKS; PROCESS ENGINEERING;

OPTIMIZATION;

EID: 84881536787     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie4000954     Document Type: Review

References (186)

1. Lahiri, S. K.; Khalfe, N.; Garawi, M. A. Process modelling and optimization strategies integrating neural networks and differential evolution Hydrocarbon Proc. 2008, Special Report) 35
2. Mohanty, S. Multiobjective optimization of synthesis gas production using non-dominated sorting genetic algorithm Comput. Chem. Eng. 2006, 30, 1019
3. Sunar, M.; Kahraman, R. A comparative study of multiobjective optimization methods in structural design Turk. J. Eng. Environ. Sci. 2001, 25, 69
4. Chen, S.; Billings, S. A. Neural networks for nonlinear dynamic system modeling and identification Int. J. Control. 1992, 56, 319
5. D'heygere, T.; Goethals, P. L. M.; De Pauw, N. Genetic algorithms for optimization of predictive ecosystems models based on decision trees and neural networks Ecol. Model 2006, 195, 20
6. Castillo, P. A.; Merelo, J. J.; Prieto, A.; Rivas, V.; Romero, G. G-Prop: Global optimization of multilayer perceptrons using GAs Neurocomputing 2000, 35, 149
7. Castillo, P. A.; Carpio, J.; Merelo, J. J.; Prieto, A.; Rivas, V. Evolving multilayer perceptrons Neural Process. Lett. 2000, 12, 115
8. Almeida, L. M.; Ludermir, T. B. A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks Neurocomputing 2010, 73, 1438
9. Benardos, P. G.; Vosniakos, G. C. Optimizing feedforward artificial neural network architecture Eng. Appl. Artif. Intell. 2007, 20, 365
10. Curteanu, S.; Cartwright, H. Neural networks applied in chemistry. I. Determination of the optimal topology of neural networks J. Chemom. 2011, 25, 527
11. Almeida, L. M.; Ludermir, T. B. Automatically searching near-optimal artificial neural networks Proc. Eur. Symp. Artif. Neural Networks (ESANN'07) 2007, 549
12. Almeida, L. M.; Ludermir, T. B. An evolutionary approach for tuning artificial neural network parameters Proc. 3rd Intl. Workshop Hybrid Artif. Intell. Syst. (HAIS'08) 2008, 156
13. Almeida, L. M.; Ludermir, T. B. An improved method for automatically searching near-optimal artificial neural networks IEEE Intl. Joint Conf. Neural Networks (IJCNN'08) (IEEE World Congress on Computational Intelligence) 2008, 2235
14. Xin, Y. Evolving artificial neural networks Proc. IEEE 1999, 87, 1423
15. Jebari, K.; Bouroumi, A.; Ettouhami, A. Parameters control in GAs for dynamic optimization Int. J. Comput. Int. Syst. 2013, 1, 47
16. Holland, J. H. Genetic algorithms and the optimal allocation of trials SIAM J. Comput. 1973, 2, 88
17. Holland, J. H. Adaptation in Natural and Artificial Systems; University of Michigan Press: Ann Arbor, MI, 1975.
18. Cartwright, H. M. The Genetic Algorithm in Science Pestic. Sci. 1995, 45, 171
19. Yan, X. F.; Chen, D. Z.; Hu, S. X. Chaos-genetic algorithms for optimizing the operating conditions based on RBF-PLS model Comput. Chem. Eng. 2003, 27, 1393
20. Cartwright, H. M.; Long, R. A. Simultaneous optimization of chemical flowshop sequencing and topology using Genetic Algorithms Ind. Eng. Chem. Res. 1993, 32, 2706
21. Cartwright, H. M.; Harris, S. P. Analysis of the distribution of airborne pollution using genetic algorithms Atmos. Environ. A-Gen. 1993, 27, 1783
22. Hancock, P. Genetic Algorithms and permutation problems: A comparison of recombination operators for neural net structure specification. Proceedings of Genetic Algorithms and Neural Networks, COGANN-92, Baltimore, Maryland, June 6, 1992.
23. Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, 1989.
24. Liu, L. B.; Wang, Y. J.; Huang, D. Designing neural networks using PSO-based memetic algorithm Proc. 4th Intl. Symp. Neural Networks (ISNN'07) 2007, 219
25. DraÌgoi, E. N.; Curteanu, S.; Fissore, D. On the use of artificial neural networks to monitor a pharmaceutical freeze-drying process Dry. Technol. 2013, 31, 72
26. Kordík, P.; Koutník, J.; Drchal, J.; Kovář ík, O.; Čepek, M.; Šnorek, M. Meta-learning approach to neural network optimization Neural Networks 2010, 23, 568
27. FurtunaÌ, R.; Curteanu, S.; Leon, F. Multi-objective optimization of a stacked neural network using NSGA-II-QNSNN algorithm Appl. Soft Comput. 2012, 12 (1) 133
28. DraÌgoi, E. N.; Curteanu, S.; Leon, F.; Galaction, A. I.; Cascaval, D. Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm Eng. Appl. Artif. Intel. 2011, 24, 1214
29. DraÌgoi, E. N.; Curteanu, S.; Fissore, D. Freeze-drying modeling and monitoring using a new neuro-evolutive technique Chem. Eng. Sci. 2012, 72, 195
30. Curteanu, S.; Nistor, A.; Curteanu, A.; Airinei, A.; Cazacu, M. Applying soft computing methods to fluorescence modelling of the polydimethylsiloxane/ silica composites containing lanthanum J. Appl. Polym. Sci. 2010, 117, 3160
31. Plagianakos, P.; Magoulas, G. D.; Nousis, N. K.; Vrahatis, M. N. Training multilayer networks with discrete activation functions. Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, Washington DC, July 15-19, 2001.
32. DraÌgoi, E. N.; Curteanu, S.; Lisa, C. A neuro-evolutive technique applied for predicting the liquid crystalline property of some organic compounds Eng. Optimiz. 2012, 44, 1261
33. He, S.; Li, X. Application of a group search optimization based Artificial Neural Network to machine condition monitoring. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Hamburg, Germany, Sept 15-18, 2008.
34. He, S.; Wu, Q. H.; Saunders, J. R. Group search optimizer: an optimization algorithm inspired by animal searching behavior IEEE Trans. Evolut. Comput. 2009, 13 (5) 973
35. Silva, D. N. G.; Pacifico, L. D. S.; Ludermir, T. B. Improved group search optimizer based on cooperation among groups for feedforward networks training with weight decay Conf. Proc. IEEE Intl. Syst. Man Cyber. 2011, 2133
36. Gao, L.; Loney, N. W. New hybrid neural network model for prediction of phase equilibrium in a two-phase extraction system Ind. Eng. Chem. Res. 2002, 41, 112
37. D'heygere, T.; Goethals, P. L. M.; De Pauw, N. Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates Ecol. Model 2003, 160, 291
38. Mouton, A. M.; Dedecker, A. P.; Lek, S.; Goethals, P. L. M. selecting variables for habitat suitability of asellus (crustacea, isopoda) by applying input variable contribution methods to artificial neural network models environ. model. assess. 2010, 15, 65
39. Shervais, S.; Zwick, M. Using reconstructability analysis to select input variables for artificial neural networks Proc. IEEE Intl. Joint Conf. Neural Networks 2003, 1-4, 3022
40. Kourentzes, N.; Crone, S. F. Frequency independent automatic input variable selection for neural networks for forecasting. 2010 International Joint Conference on Neural Networks (IJCNN 2010), July 18-23, 2010, Barcelona, Spain.
41. Storn, R. M.; Price, K. V. Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012; International Computer Science Institute: Berkley, CA, 1995.
42. Price, K. V.; Storn, R. M.; Lampien, J. A. Differential Evolution. A Practical Approach to Global Optimization; Springer: Berlin, 2005.
43. Subudhi, B.; Jena, D. Differential evolution and Levenberg Marquardt trained neural network scheme for nonlinear system identification Neural Process. Lett. 2008, 27, 285
44. Subudhi, B. A combined differential evolution and neural network approach to nonlinear system identification. Proceedings of TENCON 2008 IEEE Region 10 Conference, University of Hyderabad, India, Nov. 19-21, 2008
45. Chen, C. W.; Chen, D. Z.; Cao, G. Z. An improved differential evolution algorithm in training and encoding prior knowledge into feedforward networks with application in chemistry Chemom. Intell. Lab. 2002, 64, 27
46. Venkatraman, S.; Yen, G. G. A generic framework for constrained optimization using genetic algorithms IEEE T. Evolut. Comput. 2005, 9, 424
47. Kasat, R. B.; Gupta, S. K. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with jumping genes operator Comput. Chem. Eng. 2003, 27, 1785
48. Tang, W. J.; Wu, Q. H. Biologically inspired optimization: A review Trans. Intl. Meas. Control. 2009, 31 (6) 495
49. Ahmadi, M. A.; Shadizadeh, S. R. New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept Fuel 2012, 102, 716
50. Zhao, W. BP neural network based on PSO algorithm for temperature characteristics of gas nanosensor J. Comput. 2012, 7, 2318
51. Saraswathi, S.; Fernández-Martínez, J. L.; Kolinski, A.; Jernigan, R. L.; Kloczkowski, A. Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction J. Mol. Model 2012, 18, 4275
52. Zhao, J. H.; Liu, Z.; Dao, M. T. Reliability optimization using multiobjective ant colony system approaches Reliab. Eng. Syst. Safe 2007, 92 (1) 109
53. Goodarzi, M.; Freitas, M. P.; Vander Heyden, Y. Linear and nonlinear quantitative structure-activity relationship modeling of the HIV-1 reverse transcriptase inhibiting activities of thiocarbamates Anal. Chim. Acta 2011, 705, 166
54. Li, J.; Liu, X. Melt index prediction by RBF neural network optimized with an adaptive new ant colony optimization algorithm J. Appl. Polym. Sci. 2011, 119, 3093
55. Cartwright, H. M.; Leontjev, A. Use of a Genetic Algorithm-Neural Network hybrid in the search for high-efficiency solid-state phosphors WSEAS T. Comput. 2011, 10, 396
56. Lahiri, S. K.; Ghanta, K. C. Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline Chem. Ind. Chem. Eng. Q. 2009, 15 (2) 103
57. DraÌgoi, E. N.; Curteanu, S.; Galaction, A. I.; Caşcaval, D. Optimization methodology based on neural networks and self-adaptive differential evolution algorithm applied to an aerobic fermetation process Appl. Soft Comput. 2013, 13, 222
58. He, S.; Wu, Q. H. A novel group search optimizer inspired by animal behavioural ecology; IEEE Congress on Evolutionary Computation: Vancouver, 2006.
59. Yan, X.; Yang, W.; Shi, H. A group search optimization based on improved small world and its application on neural network training in ammonia synthesis Neurocomputing 2012, 97, 94
60. Jansen, M. A.; Kiwata, J.; Arceo, J.; Faull, K. F.; Hanrahan, G.; Porter, E. Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC Anal. Bioanal. Chem. 2010, 397, 2367
61. Lisa, G.; Apreutesei Wilson, D.; Curteanu, S.; Lisa, C.; Piuleac, C. G.; Bulacovschi, V. Ferrocene derivatives thermostability prediction using neural networks and genetic algorithms Thermochim. Acta 2011, 521 (1-2) 26
62. Cartwright, H. M. Factor-analysis of the tungsten(VI)-Rutin system Michrochem. J. 1986, 34, 313
63. Ahmad, F.; Mat-Isa, N. A.; Hussain, Z.; Boudville, R.; Osman, M. K. Genetic Algorithm-Artificial Neural Network (GA-ANN) Hybrid Intelligence for Cancer Diagnosis. Proceedings of the 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2010). 2010, 78-83.
64. Silva, C. M.; Biscaia, E. C. Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors Comput. Chem. Eng. 2003, 27, 1329
65. Guria, C.; Verma, M.; Mehrotra, S. P.; Gupta, S. K. Multi-objective optimal synthesis and design of froth flotation circuits for mineral processing, using the jumping gene adaptation of genetic algorithm Ind. Eng. Chem. Res. 2005, 44, 2621
66. Tarafder, A.; Lee, B. C. S.; Ray, A. K.; Rangaiah, G. P. Multiobjective optimization of an industrial ethylene reactor using a nondominated sorting genetic algorithm Ind. Eng. Chem. Res. 2005, 44, 124
67. Babu, B. V.; Angira, R. Modified differential evolution (MDE) for optimization, of non-linear chemical processes Comput. Chem. Eng. 2006, 30, 989
68. Kapadi, M. D.; Gudi, R. D. Optimal control of fed-batch fermentation involving multiple feeds using differential evolution Process. Biochem. 2004, 39, 1709
69. Yüzgeç, U. Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale baker's yeast fermentation process Intl. Soc. Automat. Trans. 2010, 49, 167
70. Lü, W.; Zhu, Y.; Huang, D.; Jiang, Y. A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process Chinese J. Chem. Eng. 2010, 18 (1) 66
71. Klett, G. Application of fuzzy control in chemical distillation processes Proc. 2nd IEEE Intl. Conf. Fuzzy Syst. 1993, 1, 375
72. Huang, M.; Ma, Y.; Wan, J.; Zhang, H.; Wang, Y. Modeling a paper-making wastewater treatment process by means of an adaptive network-based fuzzy inference system and principal component analysis Ind. Eng. Chem. Res. 2012, 51, 6166
73. Precup, R.-E.; Preitl, S.; Faur, G. PI predictive fuzzy controllers for electrical drive speed control: Methods and software for stable development Comput. Ind. 2003, 52, 253
74. Curteanu, S.; Leon, F. Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process Int. J. Quantum Chem. 2008, 108, 617
75. Curteanu, S.; Cazacu, M. Neural networks and genetic algorithms used for modeling and optimization of the siloxane-siloxane copolymers synthesis J. Macromol. Sci. A. 2007, A45 (1) 23
76. Suditu, G. D.; Secula, M.; Piuleac, C. G.; Curteanu, S.; Poulios, I. Genetic algorithms and neural networks based optimization applied to the wastewater decolorization by photocatalytic reaction Rev. Chim.-Bucharest 2008, 7, 816
77. Piuleac, C. G.; Curteanu, S.; Cazacu, M. Optimization by NN-GA technique of the metal complexing process-Potential application in wastewater treatment Environ. Eng. Manage. J. 2010, 9 (2) 239
78. Caliman, F. A.; Curteanu, C.; Betianu, C.; Gavrilescu, M.; Poulios, I. Neural networks and genetic algorithms optimization of the photocatalytic degradation of alcian blue 8gx J. Adv. Oxid. Technol. 2008, 11 (2) 316
79. FurtunaÌ, R.; Curteanu, S.; Racleş, C. NSGA-II-JG applied to multiobjective optimization of polymeric nanoparticles synthesis with silicone surfactants Cent. Eur. J. Chem. 2011, 9 (6) 1080
80. Lahiri, S. K.; Ghanta, K. C. Regime identification of slurry transport in pipelines-A novel modeling approach using ANN and differential evolution Chem. Ind. Chem. Eng. Q. 2010, 16 (4) 329
81. Wu, X. J.; Zhu, X. J.; Cao, G. Y.; Tu, H. Y. Predictive control of SOFC based on GA-RBF neural network model J. Power Sources 2008, 179 (1) 232
82. Malekmohammadi, B.; Kerachian, R.; Zahraie, B. Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks Environ. Modell. Software 2009, 24, 1420
83. Chen, C. W.; Chen, D. Z.; Ye, S. X. Feedforward networks based on prior knowledge and its application in modeling the true boiling point curve of the crude oil J. Chem. Eng. Chin. Univ. 2001, 15 (4) 351
84. Chen, C. W.; Chen, D. Z.; Wu, S. X. Prior-knowledge-based feedforward network simulation of true boiling point curve of crude oil Comput. Chem. 2001, 25, 541
85. Narendra, K. S.; Parthaasarathy, K. Identification and control of dynamical systems using neural networks IEEE Trans. Neural Networks 2005, 16, 8624
86. Subudhi, B.; Jena, D. A differential evolution based neural network approach to nonlinear system identification Appl. Soft Comput. 2011, 11, 861
87. Behzadian, K.; Kapelan, Z.; Savic, D.; Ardeshir, A. Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks Environ. Modell. Software 2009, 24, 530
88. Angira, R.; Babu, B. V. Performance of modified differential evolution for optimal design of complex and non-linear chemical processes J. Exp. Theor. Artif. Intell. 2006, 18, 501
89. Babu, B. V.; Chakole, P. G.; Syed Mubeen, J. H. Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor Chem. Eng. Sci. 2005, 60, 4822
90. Gujarathi, A. M.; Babu, B. V. Improved multiobjective differential evolution (MODE) approach for purified terephthalic acid (PTA) oxidation process Mater. Manuf. Process 2009, 24, 303
91. Curteanu, S.; Leon, F.; Furtuna, R.; Dragoi, E. N.; Curteanu, N. Comparison between different methods for developing neural network topology applied to a complex polymerization process. The 2010 International Joint Conference on Neural Networks IJCNN, IEEE, Barcelona, Spain, July 18-23, 2010, 1.
92. Fernandez, M.; Caballero, J.; Fernandez, L. Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM) Mol. Divers. 2011, 15, 269
93. Bernardino, H.; Barbosa, H. Artificial immune systems for optimization. In: Nature-Inspired Algorithms for Optimization: Studies in Computational Intelligence; Chiong, R., Ed.; Springer: Berlin, Heidelberg, 2009; pp 389-411.
94. Brownlee, J., Clonal selection algorithms. Technical Report 070209A; Swinburne University of Technology: Melbourne, Australia, 2007.
95. Abdul Hamid, M. B.; Abdul Rahman, T. K. Short term load forecasting using an artificial neural network trained by artificial immune system learning algorithm 12th Intl. Conf. Comput. Modell. Simulat. (UKSim) 2010, 408-413
96. Timmis, J.; Hone, A.; Stibor, T.; Clark, E. Theoretical advances in artificial immune systems Theor. Comput. Sci. 2008, 403, 11
97. Cutello, V.; Nicosia, G.; Pavone, M. Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator Proc. 2006 ACM Symp. Appl. Comput. (SAC '06) 2011, 950-954
98. Dragoi, E.; Suditu, G. D.; Curteanu, S. Modelling methodology based on Artificial Immune System algorithm and neural networks applied to removal of heavy metals from residual waters Environ. Eng. Manage. J. 2012, 11 (11) 1907
99. Blanning, R. W. The construction and implementation of metamodels Simulation 1975, 24 (6) 177
100. Fahlman, S. E. Faster-learning variations on back-propagation: An empirical study. Proceedings of the 1988 Connectionist Models Summer School; Morgan Kaufmann: Los Altos, CA, 1988.
101. Riedmiller, M.; Braun, H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In Ruspini, H., Ed.; Proceedings of the ICNN93, San Francisco, March 28-April 1, 1993, 586-591.
102. Riedmiller, M. RPROP: Description and implementation details. University of Karlsruhe: Karlsruhe, Germany, 1994.
103. Fahlman, S.; Lebière, C. The cascade-correlation learning architecture. In Neural Information Systems 2; Touretzky, D. S., Ed.; Morgan-Kaufmann: Los Altos, CA, 1990; pp 524-532.
104. Fiszelew, A.; Britos, P.; Ochoa, A.; Merlino, H.; Fernandez, E.; Garcia-Martinez, R. Finding optimal neural network architecture using genetic algorithms Adv. Comput. Sci. Eng. Res. Comput. Sci. 2007, 27, 15
105. nd ed.; Goldberg, D. E.; Koza, J. R., Eds.; Springer: Berlin, 2007.
106. Eiben, A. E.; Smith, J. E. Introduction to Evolutionary Computing; Springer: Berlin, 2003.
107. Marin, F. J.; Sandoval, F. Diseno de redes neuronales artificiales mediante algoritmos genéticos. Computacion Neuronal;, Universidad de Santiago de Compostela: Galicia, Spain, 1995; p 385.
108. Yao, X. A review of evolutionary artificial neural networks; CSIRO Australia, 1992.
109. De Falco, I.; Iazzetta, A.; Natale, P.; Tarantino, E. Evolutionary neural networks for nonlinear dynamics modelling Lecture Notes Comput. Sci. 1998, 1498, 593-602
110. Keesing, R.; Stork, D. G. Evolution and learning in neural networks: the number and distribution of learning trials affect the rate of evolution Adv. Neural Inform. Process. Syst. 1991, 3, 805
111. Vlahogianni, E. I.; Karlaftis, M. G.; Golias, J. C. Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach Transport. Res. C-Emer. 2005, 13 (3) 211
112. Kim, Y. S.; Yum, B. J. Robust design of multilayer feedforward neural networks: An experimental approach Eng. Appl. Artif. Intell. 2004, 17 (3) 249
113. Packianather, M. S.; Drake, P. R.; Rowlands, H. Optimizing the parameters of multilayered feedforward neural networks through Taguchi design of experiments Qual. Reliab. Eng. Int. 2000, 16, 461
114. Dam, M.; Saraf, D. N. Design of neural networks using genetic algorithm for on-line property estimation of crude fractionator products Comput. Chem. Eng. 2006, 30, 722
115. Sukthomya, W.; Tannock, J. The optimization of neural network parameters using Taguchi's design of experiments approach: an application in manufacturing process modeling Neural Comput. Appl. 2005, 14, 337
116. Wang, Q.; Stockton, D. J.; Baguley, P. Process cost modeling using neural networks Int. J. Prod. Res. 2000, 38 (16) 3811
117. Sureerattanan, S.; Sureerattanan, N. New Training Method and Optimal Structure of Backpropagation Networks. Advances in Natural Computation; Springer: Berlin/Heidelberg, 2005.
118. Annunziato, M.; Bertini, I.; Lucchetti, M.; Pizzuti, S. Evolving weights and transfer functions in feed forward neural networks. Proc. EUNITE 2003. European Network on Intelligent Technologies for Smart Adaptive Systems: Oulu, Finland, July 10-12, 2003.
119. Laouafi, F.; Boukadoum, A.; Leulmi, S. Reactive power dispatch with hybrid formulation: Particle swarm optimization and improved Genetic Algorithms with real coding Int. Rev. Elec. Eng. IREE 2010, 5, 601
120. Floreano, D.; Durr, P.; Mattiussi, C. Neuroevolution: From architectures to learning Evol. Intell. 2008, 1, 47
121. Ragg, T.; Gutjahr, S.; Sa, H. M. Automatic determination of optimal network topologies based on information theory and evolution 23rd EUROMICRO Conference '97 New Frontiers of Information Technology 2007, 549
122. Jang, W. H.; Hahn, J.; Hall, R. K. Genetic/quadratic search algorithm for plant economic optimizations using a process simulator Comput. Chem. Eng. 2005, 30, 285
123. Mizuta, S.; Sato, T.; Lao, D.; Ikeda, M.; Shimizu, T. Structure Design of Neural Networks Using Genetic Algorithms Complex Syst. 2001, 13, 161
124. Miller, G. F.; Todd, P. M.; Hegde, S. U. Designing neural networks using genetic algorithms. Proceedings of Third International Conference on Genetic Algorithms and Their Applications; Morgan Kaufmann: San Mateo, CA, 1989; p 379.
125. Whitely, D.; Starkweather, T.; Bogart, C. Genetic algorithm and neural networks: Optimizing connections and connectivity Parallel Comput. 1990, 14, 347
126. Gan, M.; Peng, H.; Dong, X. P. A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series prediction Appl. Math. Model 2012, 36, 2911
127. Schraudolph, N. N.; Belew, R. K. Dynamic parameter encoding for genetic algorithms Mach. Learn. 1992, 9 (1) 9
128. Kitano, H. Neurogenetic learning: An integrated method of designing and training neural networks using genetic algorithm Phys. D. 1994, 75, 225
129. Mouret, J. B.; Doncieux, S. P. MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars Evol. Intell. 2008, 1, 187
130. Harp, S. A.; Samad, T.; Guha, A. Towards the genetic synthesis of neural networks. Proceedings of Third International Conference on Genetic Algorithms and Their Applications; Morgan Kaufmann: San Mateo, CA, 1989.
131. Schaffer, J. D.; Caruna, R. A.; Eshelman, L. J. Using genetic search to exploit the emergent behavior of neural networks Phys. D. 1990, 42, 244
132. Kitano, H. Designing neural networks using genetic algorithms with graph generation system Complex Syst. 1990, 4, 461
133. White, D.; Ligomenides, P. GANNet: A genetic algorithm for optimizing topology and weights in neural network design Lect. Notes Comput. Sci. 1993, 686, 322-327
134. Plagianakos, V.; Tasoulis, D.; Vrahatis, M. A Review of Major Application Areas of Differential Evolution, In Advances in Differential Evolution; Chakraborty, U. Ed.; Springer: Berlin, 2008.
135. Boozarjomehr, R. B.; Svrcek, W. Y. Automatic design of neural network structures Comput. Chem. Eng. 2001, 25, 1075
136. Merelo, J. J.; Paton, M.; Canas, A.; Prieto, A.; Moran, F. Optimization of a competitive learning neural network by genetic algorithms Lect. Notes Comput. Sci. 1993, 686, 185-192
137. Petridis, V.; Kazarlis, S.; Papaikonomu, A.; Filelis, A. A hybrid genetic algorithm for training neural networks Artif. Neural Networks 1992, 2, 953
138. Kinnebrock, W. Accelerating the standard backpropagation method using a genetic approach Neurocomputing 1994, 6, 583
139. Yao, X.; Liu, Y. Towards designing artificial neural networks by evolution Appl. Math. Comput. 1998, 91 (1) 83
140. Cai, X.; Zhang, N.; Venayagamoorthy, G. K.; Wunschil, D. C. Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm Neurocomputing 2007, 70 (13-15) 2342
141. Dondeti, S.; Kannan, K.; Manavalan, R. Genetic algorithm optimized neural networks ensemble for estimation of mefenamic acid and paracetamol in tablets Acta Chim. Slov. 2005, 52, 440
142. Bebis, G.; Georgiopoulos, M.; Kasparis, T. Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization Neurocomputing 1997, 17, 167
143. Lahiri, S.K.; Khalfe, N. Modeling of commercial ethylene oxide reactor: A hybrid approach by artificial neural network & differential evolution Int. J. Chem. React. Eng. 2010, 8, Article A4
144. Ying, C.; Hua, M.; Zhen, J.; Shao, Z. B. Image compression using multilayer neural networks based on fast bacterial swarming algorithm. Proceedings of the Seventh International Conference on Machine Learning and Cybernetics: Kunming, China, July 12-15, 2008.
145. Weigend, A.; Rumelhart, D.; Huberman, B. Generalization by weight elimination with application to forecasting Adv. Neural Inf. Proc. Syst. 1991, 3, 875
146. Arifovic, J.; Gencay, R. Using genetic algorithms to select architecture of a feedforward artificial neural network Phys. A. 2001, 289, 574
147. Liu, Y.; Yao, X. Evolving modular neural network which generalise well. In Proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, Indiana, April 13-16, 1997.
148. Koumousis, V. K.; Katsaras, C. P. A saw-tooth genetic algorithm combing the effects of variable population size and reinitialization to enhance performance IEEE Trans. Evolut. Comput. 2006, 10, 19
149. Cao, Y. J.; Wu, Q. H. Optimisation of control parameters in genetic algorithms: A stochastic approach Int. J. Syst. Sci. 1999, 30, 551
150. Katare, S.; Bhan, A.; Caruthers, J.; Delgass, W. N. A hybrid genetic algorithm for efficient parameter estimation of large kinetic models Comput. Chem. Eng. 2004, 28, 2569
151. Kerachian, R.; Karamouz, M. Optimal reservoir operation considering the water quality issues: A stochastic conflict resolution approach Water Resour. Res. 2006, 42 (12) 1
152. Zahraie, B.; Kerachian, R.; Malekmohammadi, B. Reservoir operation optimization using adaptive varying chromosome length genetic algorithm Water Int. 2008, IWRA 33(3), 380
153. th International CSI Computer Conference (CSICC'09) Tehran, Iran. Oct. 20-21, 2009, p 536.
154. Brest, J. Constrained Real-Parameter Optimization with e-Self-Adaptive Differential Evolution. In Constraint-Handling in Evolutionary Optimization; Mezura-Montes, E., Ed.; Springer: Berlin, 2009.
155. Storn, R. On the usage of differential evolution for function optimization. In Biennial Conference of the North American Fuzzy Information Processing Society-NAFIPS; Smoth, M. H.; Lee, M. A.; Keller, J.; Yen, J., Eds.; IEEE: Berkeley, CA, 1996.
156. Fischer, M. M.; Reismann, M.; Hlavackova-Schindler, K. Parameter estimation in neural spatial interaction modelling by a derivative free global optimization method. Proceedings of IV International Conference on Geocomputation, Fredericksburg, VA, July 25-28, 1999.
157. Bhuiyan, M. Z. A.; An algorithm for determining neural network architecture using differential evolution. In Business Intelligence and Financial Engineering. International Conference on (BIFE 2009); Wabg, S.; Yu, L.; Wen, F.; He, S.; Fang, Y.; Lai, K. K., Eds.; IEE Computer Society: Washington, DC, 2009; p 3.
158. Schaffer, J. D. Multiple objective optimization with vector evaluated genetic algorithms Proc. 1st Intl. Conf. Genet. Algorithms 1985, 93
159. Fonseca, C. M.; Fleming, P. J. Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization Proc. 5th Intl. Conf. Genet. Algorithms 1993, 416
160. Horn, J.; Nafploitis, N.; Goldberg, D. E. A niched Pareto genetic algorithm for multiobjective optimization Proc. 1st IEEE Conf. Evol. Comput. 1994, 82
161. Zitzler, E.; Thiele, L. Multi objective optimization using evolutionary algorithms-A comparative case study Lect. Notes Comput. Sci. 1998, 1498, 292-301
162. Srinivas, N.; Deb, K. Multi-objective function optimization using non-dominated sorting genetic algorithms Evol. Comput. 1994, 2 (3) 221
163. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast elitist multiobjective genetic algorithm: NSGA-II IEEE Trans. Evolut. Comput. 2002, 6, 182
164. Rodriguez-Vasquez, K.; Fonseca, C. M.; Fleming, P. J. Identifying the structure of nonlinear dynamic systems using multi-objective genetic programming IEEE Trans. Syst. Man Cy. A. 2004, 34, 531
165. Qian, B.; Wang, L.; Hu, R.; Wang, W. L.; Huang, D. X.; Wang, X. A hybrid differential evolution method for permutation flow-shop scheduling Int. J. Adv. Manuf. Technol. 2008, 38, 757
166. Fan, H. Y.; Lampinen, J. A Trigonometric Mutation Operation to Differential Evolution J. Global Opt. 2003, 27, 105
167. Fan, H. Y.; Lampinen, J. A directed mutation operation for the differential evolution algorithm Int. J. Ind. Eng.-Appl. P. 2003, 1, 6
168. Zhang, Y. N.; Hu, Q. N.; Teng, H. F. Active target particle swarm optimization: Research Articles J. Concurr. Comput.-Pract. E. 2008, 20 (1) 29
169. Pant, M.; Thangaraj, R.; Abraham, A. Particle swarm optimization using adaptive mutation IEEE/DEXA'08 2008, 519
170. Zeng, J.; Hu, J.; Jie, J. Adaptive particle swarm optimization guided by acceleration information Proc. IEEE/ ICCIAS. 2006, 1, 351
171. Pampara, G.; Franken, N.; Engelbrecht, A. P. Combining particle swarm optimization with angle modulation to solve binary problems IEEE Congress Evol. Comput. 2005, 1, 89
172. Alviar, J. B.; Peña, J.; Hincapié, R. Subpopulation best rotation: A modification on PSO Rev. Facultad Ingen. 2007, 40, 118
173. Yao, X. Cooperatively Coevolving Particle Swarms for Large Scale Optimization; Conf. of EPSRC IEEE Trans. Evol. Comput. 2012, 16, 210-224
174. Zhiming, L.; Cheng, W.; Jian, L. Solving constrained optimization via a modified genetic particle swarm optimization First Intl. Workshop on Knowledge Discovery and Data Mining 2008, 217-220
175. Sedighizadeh, D.; Masehian, E. Particle Swarm Optimization Methods. Taxonomy and Applications Int. J. Comput. Theory Eng. 2009, 1 (5) 1793
176. Yu, J.; Wang, S.; Xi, L. Letters: evolving artificial neural networks using an improved PSO and DPSO Neurocomputing 2008, 71 (4-6) 1054
177. Zecchin, A. C.; Maier, H. R.; Simpson, A. R.; Roberts, A.; Berrisford, M. J.; Leonard, M. Max-Min ant system applied to water distribution system optimization. Modsim 2003-international congress on modeling and simulation; Modeling and Simulation Society of Australia and New Zealand Inc: Townsville, Australia, 2003; Vol. 2.
178. Afshar, M. H. A new transition rule for ant colony optimisation algorithms: application to pipe network optimisation problems Eng. Optimiz. 2005, 37 (5) 525
179. Afshar, M. H. Improving the efficiency of ant algorithms using adaptive refinement: application to storm water network design Adv. Water Res. 2006, 29, 1371
180. Afshar, M. H. A parameter free Continuous Ant Colony Optimization Algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach Adv. Eng. Soft. 2010, 41 (2) 188
181. Karaboga, D.; Ozturk, C. Neural networks training by artificial bee colony algorithm pattern classification Neural Network World 2009, 19, 279
182. Guand, Q.; Feng, L.; Lijuan, L. A quick group search optimizer and its application to the optimal design of double layer grid shells AIP Conf. Proc. 2009, 1233, 718-723
183. Shen, H.; Zhu, Y.; Niu, B.; Wu, Q. H. An improved group search optimizer for mechanical design optimization problems Prog. Nat. Sci. 2009, 19, 91
184. Fang, J.; Cui, Z.; Cai, X.; Zeng, J. A hybrid group search optimizer with metropolis rule 1020 Intl. Conf. Modell., Ident., Control 2010, 556-561
185. Kang, Q.; Lan, T.; Yan, Y.; Wang, L.; Wu, Q. Group search optimizer based
186. Chen, D.; Wang, J.; Zou, F.; Hou, W.; Zhao, C. An improved group search optimizer with operation of quantum-behaved swarm and its application Appl. Soft Comput. 2012, 12, 712