Coevolutionary Process Control
This text describes the use of a coevolutionary genetic algorithm (CGA) for process control. A CGA combines two artificial life techniques - life-time fitness evaluation (LTFE) and coevolution - to improve the genetic search for a neural network (NN) controlling a given process.
Here, the approach is illustrated and tested on a well-known bioreactor control problem which involves issues of delay, nonlinearity and instability.
KeywordsConstraint Satisfaction Input Node Basic Cycle Noise Pattern Genetic Search
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- C.W. Anderson and W.T. Miller. Challenging control problems. In T. Miller W, R.S. Sutton, and P. J. Werbos, editors, Neural Networks for Control. MIT Press/Bradford Books, 1991.Google Scholar
- W.D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. In C.G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artifical Life II. Addison-Wesley, California, 1992.Google Scholar
- K.S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Transaction of Neural Networks. 1(1), 1990.Google Scholar
- J. Paredis. The evolution of behaviour: Some experiments. In Meyer and Wilson, editors, From Animals to Animats. MIT Press/Bradford Books, 1991.Google Scholar
- J. Paredis. Coevolutionary constraint satisfaction. In Y. Davidor, H-P. Schwefel, and R. Manner, editors, Parallel Problem Solving from Nature III, Lecture Notes in Computer Science, volume 866. Springer-Verlag, 1994.Google Scholar
- J. Paredis. Steps towards coevolutionary classification neural networks. In R. Brooks and P. Maes, editors, Proc. Artificial Life IV. MIT Press/Bradford Books, 1994.Google Scholar
- J. Paredis. The symbiotic evolution of solutions and their representations. In L. Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms. Morgan Kaufmann Publishers, 1995.Google Scholar
- J. Paredis. Coevolutionary computation. Artificial Life Journal, 2(4), 1996.Google Scholar
- J. Paredis. Coevolutionary life-time learning. In H-M. Voigt, M. Ebeling, I. Rechenberg, and H-P. Schwefel, editors, Parallel Problem Solving from Nature IV, Lecture Notes in Computer Science, volume 1141. Springer-Verlag, Heidelberg, 1996.Google Scholar
- J. Paredis. Symbiotic coevolution for epistatic problems. In Proceedings of the European Conference on Artificial Intelligence, Chichester, 1996. John Wiley and Sons.Google Scholar
- L.H. Ungar. A bioreactor benchmark for adaptive network-based process control. In T. Miller, W.R.S. Sutton, and P.J. Werbos, editors, Neural Networks for Control. MIT Press/Bradford Books, 1991.Google Scholar
- D. Whitley. Optimizing neural networks using faster, more accurate genetic search. In Proc. Third Int. Conf. on Genetic Algorithms. Morgan Kaufmann, 1989.Google Scholar
- A.P. Wieland. Evolving controls for unstable systems. In D. Touretzky, J.L. Elman, T.J. Sejnowski, and G.E. Hinton, editors, Proc. of the 1990 Summer School. Morgan Kaufmann, 1991.Google Scholar