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Using the genetic algorithm to adapt intelligent systems

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1 Author(s)
Fogarty, T.C. ; Transputer Centre, Bristol Polytechnic., UK

The genetic algorithm, loosely based on the mechanics of evolution, is used in machine learning and optimisation problems that typically have a large search space and require a high tolerance to noise. Two examples are given of its use in the learning of rules for real-time control problems; one for adaptive rule-based optimisation of combustion in multiple-burner installations in the steel industry and the other for controlling a dynamical system. Current research on genetic algorithms is largely focussing on their use for optimising neural networks, since this is a natural way of combining the paradigms of evolution and learning, and on parallel and distributed implementations, to facilitate the efficient solution of larger problems. A project using a parallel implementation of an incremental genetic algorithm to generate constraint networks from raw data is described

Published in:

Symbols Versus Neurons, IEE Colloquium on

Date of Conference:

1 Oct 1990

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