Abstract
The paper presents various evolved neurocontrollers for the pole-balancing problem with good benchmark performance. They are small neural networks with recurrent connectivity. The applied evolutionary algorithm, which is not based on genetic algorithms, was designed to evolve neural networks with arbitrary connectivity. It uses no quantization of inputs, outputs or internal parameters, and sets no constraints on the number of neurons. Network topology and parameters like weights and bias terms are developed simultaneously.
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Albrecht, R. F., Reeves, C. R., and Steele, N. C. (eds.), Artificial Neural Nets and Genetic Algorithms, Proceedings of the International Conference in Innsbruck, Austria, 1993, Springer, Wien 1993.
Anderson, C. W. (1989). Learning to control an inverted pendulum using neural networks. IEEE Control Systems Magazine, 9, 31–37.
Barto, A. G., Sutton, R. S., and Anderson, C. W. (1983). Neuronlike adaptive elements that solve difficult learning control problems. IEEE Transactions on Systems, Man, Cybernetics, 13, 834–846.
Bapi, R. S., D'Cruz, B., and Bugmann, G. (1996) Neuro-resistive grid approach to trainable controllers: A pole balancing example, submitted to Neural Computing and Applications.
Dasgupta, D., and McGregor, D. R. (1993). Evolving neurocontrollers for pole balancing. In S. Gielen and B. Kappen (eds.), ICANN'93 Proceedings of the International Conference on Artificial Neural Networks, Berlin: Springer-Verlag, 1993, pp. 834–837.
Dieckmann, U. (1995), Coevolution as an autonomous learning strategy for neuromodules, in: Herrmann, H., Pöppel, E., and Wolf, D. (eds.), Supercomputing in Brain Research-From Tomography to Neural Networks, Singapore: World Scientific, (pp. 331–347).
Pasemann, F., Dieckmann, U. (1997). Evolved Neurocontrollers for pole-balancing. In: Proceedings IWANN'97, Lanzarote, Spain, June 4–6, 1997, Lecture Notes in Computer Science. Berlin: Springer-Verlag.
Pasemann, F., Nelle, E. (1993). Elements of non-convergent neurodynamics, in: Andersson, S. L, Andersson, A.E., Ottoson, U.: Dynamical Systems-Theory and Applications. Singapore: World Scientific.
Geva, S., and Sitte, J. (1993). A cartpole experiment benchmark for trainable controllers, IEEE Control Systems Magazin, 13, 40–51.
Schaffer, J. D., Whitley, D., and Eshelman, L. J. (1992). Combination of genetic algorithms and neural networks: A survey of the state of the art. In: Proceedings International Workshop on combinations of genetic algorithms and neural networks (COGANN-92), Los Alamitos, CA, IEEE Computer Society Press.
Yao, X. (1993). A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 8, 539–567.
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© 1997 Springer-Verlag Berlin Heidelberg
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Pasemann, F. (1997). Pole-balancing with different evolved neurocontrollers. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020256
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DOI: https://doi.org/10.1007/BFb0020256
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