Krause, P.Krone, A.Lindenblatt, M.Slawinski, T.2004-12-072004-12-0719982001-10-16http://hdl.handle.net/2003/536010.17877/DE290R-15267In this paper we propose a hybrid fuzzy-evolutionary system for fuzzy modelling in high dimensional spaces. The system architecture is based on a Michigan-style approach (one individual represents one fuzzy rule). The design of the evolutionary algorithm makes use of a distance measure in the search space that in turn reflects some heuristic assumptions about the fitness landscape. Additionally, strategy parameters are dynamically adapted by means of a fuzzy controller. The approach is successfully applied to a complex benchmark problem as well as to several real-world modelling tasks such as the cancellation behaviour of insurance clients and the classification of automatic gearboxes.enUniversität DortmundReihe Computational Intelligence ; 50004report