Dynamic systems modelling using genetic programming

In this contribution genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon (Int. J. Control 44 (1986) 235) were used in this work.

[1]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[2]  M. Ehrgott.  Multiobjective Optimization , 2008, AI Mag..

[3]  Prabhat Hajela,et al.  Genetic search strategies in multicriterion optimal design , 1991 .

[4]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[5]  Yaman Arkun,et al.  Control of nonlinear systems using polynomial ARMA models , 1993 .

[6]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[7]  J. Gibbons,et al.  Nonparametric Methods for Quantitative Analysis. , 1997 .

[8]  G. Bierman.  Factorization methods for discrete sequential estimation , 1977 .

[9]  Stephen A. Billings,et al.  Nonlinear model validation using correlation tests , 1994 .

[10]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[11]  Peter J. Fleming,et al.  System identification strategies applied to aircraft gas turbine engines , 2000 .

[12]  David E. Goldberg,et al.  A niched Pareto genetic algorithm for multiobjective optimization , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[13]  Lishan Kang,et al.  The Kinetic Evolutionary Modeling of Complex Systems of Chemical Reactions , 1999, Comput. Chem..

[14]  A. J. Morris,et al.  Artificial neural networks in process engineering , 1991 .

[15]  Stephen A. Billings,et al.  Non-linear system identification using neural networks , 1990 .

[16]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[17]  Mark J. Willis,et al.  DYNAMIC MODELLING USING GENETIC PROGRAMMING , 2002 .

[18]  S. Billings,et al.  Correlation based model validity tests for non-linear models , 1986 .

[19]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[20]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[21]  Sheng Chen,et al.  Representations of non-linear systems: the NARMAX model , 1989 .

[22]  Mark J. Willis,et al.  Modeling and control of a food extrusion process , 1997 .

[23]  J. B. Gomm,et al.  Experiment design considerations for non-linear system identification using neural networks , 1997 .

[24]  Gary B. Lamont,et al.  Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art , 2000, Evolutionary Computation.

[25]  Hisao Ishibuchi,et al.  Multi-objective genetic local search algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[26]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[27]  S. A. Billings,et al.  Multi-directional model validity tests for non-linear system identification , 2000 .