Evolving structure and function of neurocontrollers
Frank Pasemann, Ulf Dieckmann, and Ulrich Steinmetz
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Submission date: 04. May. 1999
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The presented evolutionary algorithm is especially designed to generate recurrent neural networks with non-trivial internal dynamics. It is not based on genetic algorithms, and sets no constraints on the number of neurons and the architecture of a network. Network topology and parameters like synaptic weights and bias terms are developed simultaneously. It is well suited for generating neuromodules acting in sensorimotor loops, and therefore it can be used for evolution of neurocontrollers solving also nonlinear control problems. We demonstrate this capability by applying the algorithm successfully to the following task: A rotating pendulum is mounted on a cart; stabilize the rotator in an upright position, and center the cart in a given finite interval.