Using the genetic algorithm to adapt intelligent systems

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