- author = "Michael Affenzeller and Stephan M. Winkler and Gabriel Kronberger and Michael Kommenda and Bogdan Burlacu and Stefan Wagner",
- title = "Gaining Deeper Insights in Symbolic Regression",
- booktitle = "Genetic Programming Theory and Practice XI",
- year = "2013",
- series = "Genetic and Evolutionary Computation",
- editor = "Rick Riolo and Jason H. Moore and Mark Kotanchek",
- publisher = "Springer",
- chapter = "10",
- pages = "175--190",
- address = "Ann Arbor, USA",
- month = "9-11 " # may,
- keywords = "genetic algorithms, genetic programming, Symbolic regression, Algorithm analysis, Population diversity Building block analysis, Genealogy, Variable networks",
- isbn13 = "978-1-4939-0374-0",
-
DOI = "
doi:10.1007/978-1-4939-0375-7_10",
- 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 scrutinised 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 generalisation. In order to gain more information about the process we monitor and analyse 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.",
-
notes = "
Part of \cite{Riolo:2013:GPTP} published after the workshop in 2013",
