Seeding GP Populations
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{langdon:2000:seed,
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author = "W. B. Langdon and J. P. Nordin",
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title = "Seeding {GP} Populations",
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booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
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year = "2000",
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editor = "Riccardo Poli and Wolfgang Banzhaf and
William B. Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
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volume = "1802",
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series = "LNCS",
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pages = "304--315",
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address = "Edinburgh",
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publisher_address = "Berlin",
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month = "15-16 " # apr,
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organisation = "EvoNet",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Pareto
multi-objective fitness: Poster",
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ISBN = "3-540-67339-3",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_eurogp2000_seed.pdf",
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URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL_eurogp2000_seed.ps.gz",
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DOI = "doi:10.1007/978-3-540-46239-2_23",
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video_url = "http://www.cs.ucl.ac.uk/staff/W.Langdon/benelearn99/27-sep-99.5-oct.vanP5000.101-animation.gif",
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size = "12 pages",
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abstract = "We show GP populations can evolve from perfect
programs which match the training material under the
influence of a Pareto multi-objective fitness and
program size selection scheme to generalise. The
technique is demonstrated upon programmatic image
compression, two machine learning benchmark problems
(Pima Diabetes and Wisconsin Breast Cancer) and a
consumer profiling task (Benelearn99).",
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notes = "Benelearn99 binary classification caravan insurance
dataset includes missing data, it now forms CoIL-2000
https://archive.ics.uci.edu/ml/datasets/Insurance+Company+Benchmark+%28COIL+2000%29
https://liacs.leidenuniv.nl/~puttenpwhvander/library/cc2000/
27-sep-99.5-oct.vanP5000.101-animation.gif from
\cite{langdon:1999:benelearn1}
EuroGP'2000, part of \cite{poli:2000:GP}",
- }
Genetic Programming entries for
William B Langdon
Peter Nordin
Citations