Abstract
A distinguishing feature of symbolic regression using genetic programming is its ability to identify complex nonlinear white-box models. This is especially relevant in practice where models are extensively scrutinized in order to gain knowledge about underlying processes. This potential is often diluted by the ambiguity and complexity of the models produced by genetic programming. In this contribution we discuss several analysis methods with the common goal to enable better insights in the symbolic regression process and to produce models that are more understandable and show better generalization. In order to gain more information about the process we monitor and analyze the progresses of population diversity, building block information, and even more general genealogy information. Regarding the analysis of results, several aspects such as model simplification, relevance of variables, node impacts, and variable network analysis are presented and discussed.
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The work described in this chapter was done within the Josef Ressel Center for Heuristic Optimization Heureka! sponsored by the Austrian Research Promotion Agency (FFG).
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Affenzeller, M., Winkler, S.M., Kronberger, G., Kommenda, M., Burlacu, B., Wagner, S. (2014). Gaining Deeper Insights in Symbolic Regression. In: Riolo, R., Moore, J., Kotanchek, M. (eds) Genetic Programming Theory and Practice XI. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0375-7_10
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