Linear Combinations of Features As Leaf Nodes in Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Zegklitz:2017:GECCO,
-
author = "Jan Zegklitz and Petr Posik",
-
title = "Linear Combinations of Features As Leaf Nodes in
Symbolic Regression",
-
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
-
series = "GECCO '17",
-
year = "2017",
-
isbn13 = "978-1-4503-4939-0",
-
address = "Berlin, Germany",
-
pages = "145--146",
-
size = "2 pages",
-
URL = "http://doi.acm.org/10.1145/3067695.3076009",
-
DOI = "doi:10.1145/3067695.3076009",
-
acmid = "3076009",
-
publisher = "ACM",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, symbolic
regression",
-
month = "15-19 " # jul,
-
abstract = "We propose a new type of leaf node for use in Symbolic
Regression (SR) that performs linear combinations of
feature variables (LCF). LCF's weights are tuned using
a gradient method based on back-propagation algorithm
known from neural networks. Multi-Gene Genetic
Programming (MGGP) was chosen as a baseline model. As a
sanity check, we experimentally show that LCFs improve
the performance of the baseline on a rotated toy SR
problem. We then perform a thorough experimental study
on a number of artificial and real-world SR benchmarks.
The usage of LCFs in MGGP statically improved the
results in 5 cases out of 9, while it worsen them in
only a single case.",
-
notes = "Also known as \cite{Zegklitz:2017:LCF:3067695.3076009}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Jan Zegklitz
Petr Posik
Citations