Optimising Optimisers with Push GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Lones:2020:EuroGP,
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author = "Michael Lones",
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title = "Optimising Optimisers with {Push GP}",
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booktitle = "EuroGP 2020: Proceedings of the 23rd European
Conference on Genetic Programming",
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year = "2020",
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month = "15-17 " # apr,
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editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
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series = "LNCS",
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volume = "12101",
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publisher = "Springer Verlag",
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address = "Seville, Spain",
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pages = "101--117",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Optimisation,
Metaheuristics",
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isbn13 = "978-3-030-44093-0",
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URL = "https://arxiv.org/abs/1910.00945",
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video_url = "https://www.youtube.com/watch?v=pRvH7CdbFDo",
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DOI = "doi:10.1007/978-3-030-44094-7_7",
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size = "16 pages",
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abstract = "This work uses Push GP to automatically design both
local and population-based optimisers for
continuous-valued problems. The optimisers are trained
on a single function optimisation landscape, using
random transformations to discourage overfitting. They
are then tested for generality on larger versions of
the same problem, and on other continuous-valued
problems. In most cases, the optimisers generalise well
to the larger problems. Surprisingly, some of them also
generalise very well to previously unseen problems,
outperforming existing general purpose optimisers such
as CMA-ES. Analysis of the behaviour of the evolved
optimisers indicates a range of interesting
optimisation strategies that are not found within
conventional optimisers, suggesting that this approach
could be useful for discovering novel and effective
forms of optimisation in an automated manner.",
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notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of
\cite{Hu:2020:GP} EuroGP'2020 held in conjunction with
EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
- }
Genetic Programming entries for
Michael A Lones
Citations