Classifier systems for enhancing neural network-based global function approximations
P. Hajela, Beomjoon Kim · 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization · 1998
Soft-computing tools such as genetic algorithms and neural networks have found increased use in problems of engineering design. Applications have included the use of neural networks as response surface-like function approximations, and in identifying causality in numerical data. Both neural networks and genetic algorithms have also been used in the solution of generically difficult design optimization problems. A more recent application of these tools is in the field of computational intelligence. The present paper explores the use of a machine learning paradigm referred to as a classifier system, the central building block of which is a genetic algorithm, to provide a rational approach through which to construct neural network based global function approximations. The emphasis of the approach is to determine through computations, an optimal distribution of data which maximally improves the quality of the function approximation available from a back-propagation neural network. Numerical results are presented in support of the proposed scheme.