A preliminary study of Prediction Interval Methods with Genetic Programming
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- @InProceedings{rebuli:2022:GECCOcomp,
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author = "Karina {Brotto Rebuli} and Mario Giacobini and
Niccolo Tallone and Leonardo Vanneschi",
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title = "A preliminary study of Prediction Interval Methods
with Genetic Programming",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference Companion",
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year = "2022",
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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",
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pages = "530--533",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, modelling
uncertainty, crisp prediction, prediction interval",
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isbn13 = "978-1-4503-9268-6/22/07",
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DOI = "doi:10.1145/3520304.3528806",
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abstract = "This article presents an exploratory study on
modelling Prediction Intervals (PI) with two Genetic
Programming (GP) methods. A PI is the range of values
in which the real target value is expected to fall
into. It should combine two contrasting properties: to
be as narrow as possible and to include as many data
observations as possible. One proposed GP method,
called CWC-GP, evolves simultaneously the lower and
upper boundaries of the PI using a single fitness
measure that combines the width and the probability
coverage of the PI. The other proposed GP method,
called LUBE-GP, evolves independently the boundaries of
the PI with a multi-objective approach, in which one
fitness aims to minimise the width and the other aims
to maximise the probability coverage of the PI. Both
methods were applied with Direct and Sequential
approaches. In the former, the PI is assessed without
the crisp prediction of the model. In the latter, the
method makes use of the crisp prediction to find the PI
boundaries. The proposed methods showed to have good
potential on assessing PIs and the presented
preliminary results pave the way to further
investigations. The most promising results were
observed with the Sequential CWC-GP.",
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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
Karina Brotto Rebuli
Mario Giacobini
Niccolo Tallone
Leonardo Vanneschi
Citations