Exhaustive Symbolic Regression
Created by W.Langdon from
gpbibliography.bib Revision:1.8010
 @Article{Bartlett:TEVC,

author = "Deaglan J. Bartlett and Harry Desmond and
Pedro G. Ferreira",

journal = "IEEE Transactions on Evolutionary Computation",

title = "Exhaustive Symbolic Regression",

year = "2024",

volume = "28",

number = "4",

pages = "950964",

month = aug,

keywords = "genetic algorithms, genetic programming, Mathematical
models, Complexity theory, Optimisation, Numerical
models, Biological system modelling, Standards, Search
problems, Symbolic regression, data analysis, minimum
description length, MDL, model selection, cosmology",

DOI = "doi:10.1109/TEVC.2023.3280250",

ISSN = "19410026",

size = "15 pages",

abstract = "Symbolic Regression (SR) algorithms attempt to learn
analytic expressions which fit data accurately and in a
highly interpretable manner. Conventional SR suffers
from two fundamental issues which we address here.
First, these methods search the space stochastically
(typically using genetic programming) and hence do not
necessarily find the best function. Second, the
criteria used to select the equation optimally
balancing accuracy with simplicity have been variable
and subjective. To address these issues we introduce
Exhaustive Symbolic Regression (ESR), which
systematically and efficiently considers all possible
equationsmade with a given basis set of operators and
up to a specified maximum complexity and is therefore
guaranteed to find the true optimum (if parameters are
perfectly optimised) and a complete function ranking
subject to these constraints. We implement the minimum
description length principle as a rigorous method for
combining these preferences into a single objective. To
illustrate the power of ESR we apply it to a catalogue
of cosmic chronometers and the Pantheon+ sample of
supernovae to learn the Hubble rate as a function of
redshift, finding 40 functions (out of 5.2 million
trial functions) that fit the data more economically
than the Friedmann equation. These lowredshift data
therefore do not uniquely prefer the expansion history
of the standard model of cosmology. We make our code
and full equation sets publicly available.",

notes = "Also known as \cite{10136815}
Institute of Cosmology and Gravitation, University of
Portsmouth, PO1 3FX Portsmouth, UK",
 }
Genetic Programming entries for
Deaglan J Bartlett
Harry Desmond
Pedro G Ferreira
Citations