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International Journal of Systems Science Volume 47, 2016 - Issue 7
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Original Articles

Modelling and prediction of complex non-linear processes by using Pareto multi-objective genetic programming

A. JamaliDepartment of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, IranCorrespondence[email protected]
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,
E. KhaleghiDepartment of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, IranView further author information
,
I. GholaminezhadDepartment of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, IranView further author information
&
N. Nariman-zadehDepartment of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, IranView further author information
Pages 1675-1688 | Received 19 Oct 2013, Accepted 27 Jun 2014, Published online: 07 Aug 2014

References

  • Chan, K.Y., Kwong, C.K., Dillon T.S., & Tsim, Y.C. (2013). Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming. Journal of Applied Soft Computing, 11, 1648–1656.
  • Coelho, A.L.V., Fernandes E., & Faceli, K. (2010). Inducing multi-objective clustering ensembles with genetic programming. Journal of Neurocomputing, 74, 494–498.
  • Grosman, B., & Lewin, D.R. (2004). Adaptive genetic programming for steady-state process modeling. Journal of Computers and Chemical Engineering, 28, 2779–2790.
  • Hajipour, V., Kazemi, A., & Mousavi, S.M. (2013). A fuzzy expert system to increase accuracy and precision in measurement system analysis. Measurement, 46, 2770–2780.
  • Hinchliffe, M.P., & Willis, M.J. (2003). Dynamic systems modelling using genetic programming. Journal of Computers and Chemical Engineering, 27, 1841–1854.
  • Jamali, A., Hajiloo, A., & Nariman-zadeh, N. (2010). Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA). Expert Systems with Applications, 37, 401–413.
  • Jamali, A., Motevalli, S.J., & Nariman-zadeh, N. (2013). Extracting fuzzy rules for modeling of complex processes by using neural networks, Proceedings of the Institution of Mechanical Engineers, Part C. Journal of Mechanical Engineering Science, 227, 2861–2869.
  • Jamali, A., Nariman-zadeh, N., & Atashkari, K. (2008). Multi-objective Uniform diversity Genetic Algorithm (MUGA). In W. Kosinski (Ed.), Advances in evolutionary algorithms (pp. 297–314). Vienna: IN-TECH.
  • Jamali, A., Nariman-zadeh, N., Darvizeh, A., Masoumi, A., & Hamrang, S. (2009). Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Engineering Applications of Artificial Intelligence, 22, 676–687.
  • Jamali, A., Salehpour, M., & Nariman-zadeh, N. (2013). Robust Pareto active suspension design for vehicle vibration model with probabilistic uncertain parameters. Multibody System Dynamics, 30, 265–285.
  • Koza, J. (1992). Genetic programming, on the programming of computers by means of natural selection. Cambridge: MIT Press.
  • Metenidis, M.F., Witczak, M., & Korbicz, J. (2004). A novel genetic programming approach to nonlinear system modelling: Application to the DAMADICS benchmark problem. Engineering Applications of Artificial Intelligence, 17, 363–370.
  • Nariman-zadeh, N, & Jamali, A. (2007). Pareto genetic design of GMDH-type neural networks for nonlinear systems. IWIM 2007, Prague.
  • Sreekanth, J., & Datta, B. (2010). Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models. Journal of Hydrology, 393, 245–256.
  • Togan, N., & Baysec, S. (2010). Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine. Journal of Applied Energy, 87, 3401–3408.
  • Zhao, H. (2007). A multi-objective genetic programming approach to developing Pareto optimal decision trees. Journal of Decision Support Systems, 43, 809–826.

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