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:1.8028
- @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