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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© 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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