Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Sijben:2022:GECCO,
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author = "Evi Sijben and Tanja Alderliesten and Peter Bosman",
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title = "Multi-modal multi-objective model-based genetic
programming to find multiple diverse high-quality
models",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference",
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year = "2022",
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editor = "Alma Rahat and Jonathan Fieldsend and
Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and
Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and
Erik Hemberg and Christopher Cleghorn and Chao-li Sun and
Georgios Yannakakis and Nicolas Bredeche and
Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and
Sebastian Risi and Laetitia Jourdan and
Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and
John Woodward and Malcolm Heywood and Elizabeth Wanner and
Leonardo Trujillo and Domagoj Jakobovic and
Risto Miikkulainen and Bing Xue and Aneta Neumann and
Richard Allmendinger and Inmaculada Medina-Bulo and
Slim Bechikh and Andrew M. Sutton and
Pietro Simone Oliveto",
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pages = "440--448",
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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, Evolutionary
Machine Learning, multi-objective, multi-modal,
multi-tree",
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isbn13 = "978-1-4503-9237-2",
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DOI = "doi:10.1145/3512290.3528850",
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abstract = "Explainable artificial intelligence (XAI) is an
important and rapidly expanding research topic. The
goal of XAI is to gain trust in a machine learning (ML)
model through clear insights into how the model arrives
at its predictions. Genetic programming (GP) is often
cited as being uniquely well-suited to contribute to
XAI because of its capacity to learn (small) symbolic
models that have the potential to be interpreted.
Nevertheless, like many ML algorithms, GP typically
results in a single best model. However, in practice,
the best model in terms of training error may well not
be the most suitable one as judged by a domain expert
for various reasons, including overfitting, multiple
different models existing that have similar accuracy
and unwanted errors on particular data points due to
typical accuracy measures like mean squared error.
Hence, to increase chances that domain experts deem a
resulting model plausible, it becomes important to be
able to explicitly search for multiple, diverse,
high-quality models that trade-off different meanings
of accuracy. In this paper, we achieve exactly this
with a novel multi-modal multi-tree multi-objective GP
approach that extends a modern model-based GP algorithm
known as GP-GOMEA that is already effective at
searching for small expressions.",
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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
Evi Sijben
Tanja Alderliesten
Peter A N Bosman
Citations