Single and multi-objective genetic programming design for B-spline neural networks and neuro-fuzzy systems

The design phase of B-spline neural networks and neuro-fuzzy systems is a highly computationally complex task. Existent heuristics, namely the ASMOD algorithm, have been found to be highly dependent on the initial conditions employed. A Genetic Programming approach is proposed, which produces an efficient topology search, obtaining additionally more consistent solutions. The facility to incorporate a multi-objective approach to the GP algorithm is exploited, enabling the designer to obtain better conditioned models, and more adequate for their intended use.