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Feature selection and architecture optimization are two key tasks in most neural network applications. Appropriate input features must be selected from a given (and often large) set of possible features and architecture parameters of the network such as the number of hidden neurons or learning parameters must be adapted with respect to the selected features and a learning data set. This article sets out an evolutionary algorithm (EA) that performs the tasks simultaneously for radial basis function (RBF) networks. The feasibility and the benefits of this approach are demonstrated in an application in the area of computer security: the detection of attacks (intrusive behavior) in computer networks. The EA, however, is independent from the application example given so that the ideas and solutions may easily be transferred to other applications and even other neural network paradigms. In the application example investigated overall classification rates of about 99.4% (average of eight attack types) can be reached for independent validation data.