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Abstract
The genetic algorithm is used for the learning of prototype control rules for a dynamic system. Prototype control rules are point based, but only a limited number of points in the state space with associated control actions are learned. The nearest-neighbour algorithm is used to decide which of the rules to fire in any situation. The example of a simulated cart-pole balancing problem is used to demonstrate the advantages of this approach over other rule-learning methods.
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