Active Learning Improves Performance on Regression Tasks inStackGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{haut:2022:GECCOcomp,
-
author = "Nathaniel Haut and Wolfgang Banzhaf and Bill Punch",
-
title = "Active Learning Improves Performance on Regression
Tasks {inStackGP}",
-
booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference Companion",
-
year = "2022",
-
editor = "Heike Trautmann and Carola Doerr and
Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and
Marcus Gallagher and Yew-Soon Ong and
Abhishek Gupta and Anna V Kononova and Hao Wang and
Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and
Fabio Caraffini and Johann Dreo and Anne Auger and
Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Tea Tusar and Dimo Brockhoff and Tome Eftimov and
Pascal Kerschke and Boris Naujoks and Mike Preuss and
Vanessa Volz and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Mark Coletti and Catherine (Katie) Schuman and
Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and
Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and
Richard Allmendinger and Jussi Hakanen and
Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and
John McCall and Jaume Bacardit and
Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and
David Walker and Jamal Toutouh and UnaMay O'Reilly and
Penousal Machado and Joao Correia and Sergio Nesmachnow and
Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and
Francisco {Fernandez de Vega} and Giuseppe Paolo and
Alex Coninx and Antoine Cully and Adam Gaier and
Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and
Vesna Nowack and Aymeric Blot and Emily Winter and
William B. Langdon and Justyna Petke and
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and
Thomas Stuetzle and David Paetzel and
Alexander Wagner and Michael Heider and Nadarajen Veerapen and
Katherine Malan and Arnaud Liefooghe and Sebastien Verel and
Gabriela Ochoa and Mohammad Nabi Omidvar and
Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and
Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and
Jean-Baptiste Mouret and Stephane Doncieux and
Stefanos Nikolaidis and Julian Togelius and
Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and
Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and
Ofer Shir and Lee Spector and Alma Rahat and
Richard Everson and Jonathan Fieldsend and Handing Wang and
Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and
Michael Kommenda and William {La Cava} and
Gabriel Kronberger and Steven Gustafson",
-
pages = "550--553",
-
address = "Boston, USA",
-
series = "GECCO '22",
-
month = "9-13 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, symbolic
regression, active learning",
-
isbn13 = "978-1-4503-9268-6/22/07",
-
DOI = "doi:10.1145/3520304.3528941",
-
abstract = "This paper introduces an active learning method for
symbolic regression using StackGP. The approach begins
with a small number of data points for StackGP to
model. To improve the model the system incrementally
adds the data point characterized by maximizing
prediction uncertainty as measured by the model
ensemble. Symbolic regression is re-run with the larger
data set. This cycle continues until the system
satisfies a termination criterion. The Feynman AI
benchmark set of equations is used to examine the
ability of the method to find appropriate models using
as few data points as possible. The approach
successfully rediscovered 72 of the 100 Feynman
equations without the use of domain expertise or data
translation.",
-
notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Nathaniel Haut
Wolfgang Banzhaf
William F Punch
Citations