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Evolving Dynamical Neural Networks for Adaptive Behavior

Department of Computer Engineering and Science Department of Biology Case Western Reserve University Cleveland, OH 44106

John C. Gallagher

Department of Computer Engineering and Science Case Western Reserve University Cleveland, OH 44106

We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but that can operate in its absence if necessary.

Key Words: neural networks • genetic algorithms • motor control • chemotaxis, locomotion

Adaptive Behavior, Vol. 1, No. 1, 91-122 (1992)
DOI: 10.1177/105971239200100105


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