Created by W.Langdon from gp-bibliography.bib Revision:1.8098
SR bench github/pull based, hundreds of problems. Penn Machine Learning Benchmark PMLB
p3 'Eureqa ... we implement its underlying algorithms in an open-source package'
p4 'We required contributors to implement a minimal, Scikit-learn compatible, Python API ... compatible with ... https://www.sympy.org/en/index.html ... [must] be managed via www.anaconda.com/'
Table 2 'Computing budget: 1290000 core hours, 436800 core hours' (total 197 core years).
'Semantic backpropagation (SBP)...affine transformations'
'FFX \cite{McConaghy:2011:GPTP} simply initializes a population of equations, selects the Pareto optimal set, and returns a single linear model by treating the population of equations as features.'
p7 'Feynman Symbolic Regression Database, and the ODE-Strogatz repository'
{+, −, ∗, /, sin, cos, arcsin, arccos, exp, log, pow, max, min}. also 'simplifying via sympy'.
p8 'models generated by Operon are significantly more accurate' ... 'models produced by FEAT are significantly smaller'.
p19 'Operon C++ \cite{Kommenda:GPEM}, GP-GOMEA \cite{Virgolin:EC}, and DSR \cite{Petersen:2020:ICLR}, , which taken together give the set of best trade-offs between accuracy and simplicity across the black-box regression problems'
See also \cite{dick:2022:SymReg}
Also known as \cite{NEURIPS DATASETS AND BENCHMARKS2021_c0c7c76d} \cite{DBLP:journals/corr/abs-2107-14351},",
Genetic Programming entries for William La Cava Patryk Orzechowski Bogdan Burlacu Fabricio Olivetti de Franca Marco Virgolin Ying Jin Michael Kommenda Jason H Moore