Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Kammerer:2019:GPTP,

• author = "Lukas Kammerer and Gabriel Kronberger and Bogdan Burlacu and Stephan M. Winkler and Michael Kommenda and Michael Affenzeller",
• title = "Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication",
• booktitle = "Genetic Programming Theory and Practice XVII",
• year = "2019",
• editor = "Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel",
• pages = "79--99",
• address = "East Lansing, MI, USA",
• month = "16-19 " # may,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Grammar enumeration, Graph search",
• isbn13 = "978-3-030-39957-3",
• DOI = "doi:10.1007/978-3-030-39958-0_5",
• abstract = "Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. Such scenarios often require specific model properties such as interpretability, robustness, trustworthiness and plausibility, that are not easily achievable using standard approaches like genetic programming for symbolic regression. In this chapter we introduce a deterministic symbolic regression algorithm specifically designed to address these issues. The algorithm uses a context-free grammar to produce models that are parameterized by a non-linear least squares local optimization procedure. A finite enumeration of all possible models is guaranteed by structural restrictions as well as a caching mechanism for detecting semantically equivalent solutions. Enumeration order is established via heuristics designed to improve search efficiency. Empirical tests on a comprehensive benchmark suite show that our approach is competitive with genetic programming in many noiseless problems while maintaining desirable properties such as simple, reliable models and reproducibility.",
• notes = "Part of \cite{Banzhaf:2019:GPTP}, published after the workshop",

}

Genetic Programming entries for Lukas Kammerer Gabriel Kronberger Bogdan Burlacu Stephan M Winkler Michael Kommenda Michael Affenzeller

Citations