This paper presents how neural swimming controllers for a simulated lamprey can be developed using evolutionary algorithms. A Genetic Algorithm is used for evolving the architecture of a connectionist model which determines the muscular activity of a simulated body. This work is inspired by the biological model developed by Ekeberg which reproduces the Central Pattern Generator observed in the real lamprey [Ekeberg 93]. In evolving artificial controllers, we demonstrate that a Genetic Algorithm can be an interesting design technique for neural controllers and that there exist alternative solutions to the biological connectivity. A variety of neural controllers are evolved which can produce the pattern of oscillations necessary for swimming. These patterns can be modulated through the external excitation applied to the network in order to vary the speed and the direction of swimming. The best evolved controllers cover larger ranges of frequencies, phase lags and speeds of swimming than ...
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