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In the first part of this work, this approach is described in detail. Important issues are the prevention of bloat and overfitting, the simplification of models, and the identification of relevant input variables. In this context, different methods for bloat control are presented and compared. In addition, a novel way to detect and reduce over fitting is presented and analysed.
The second part of this work demonstrates how comprehensive symbolic regression can be applied for analysis of real-world systems. Variable interaction networks for a blast furnace process and an industrial chemical process are presented and discussed. Additionally, the same approach is also applied on an economic data set to identify macro-economic dependencies.",
Genetic Programming entries for Gabriel Kronberger