Automated, Explainable Rule Extraction from MAP-Elites archives
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Urquhart:2021:evoapplications,
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author = "Neil Urquhart and Emma Hart and Silke Hoehl",
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title = "Automated, Explainable Rule Extraction from
{MAP-Elites} archives",
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booktitle = "24th International Conference, EvoApplications 2021",
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year = "2021",
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month = "7-9 " # apr,
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editor = "Pedro Castillo and Juanlu Jimenez-Laredo",
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series = "LNCS",
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volume = "12694",
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publisher = "Springer Verlag",
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address = "virtual event",
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pages = "258--272",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-72698-0",
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DOI = "doi:10.1007/978-3-030-72699-7_17",
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abstract = "Quality-diversity (QD) algorithms that return a large
archive of elite solutions to a problem provide
insights into how high-performing solutions are
distributed throughout a feature-space defined by a
user. they are often described as illuminating the
feature-space, providing a qualitative illustration of
relationships between features and objective quality.
However, if there are 1000s of solutions in an archive,
extracting a succinct set of rules that capture these
relationships in a quantitative manner (i.e. as a set
of rules) is challenging. We propose two methods for
the automated generation of rules from data contained
in an archive; the first uses Genetic Programming and
the second, a rule-induction method known as CN2. Rules
are generated from large archives of data produced by
running MAP-Elites on an urban logistics problem. A
quantitative and qualitative evaluation that includes
the end-user demonstrate that the rules are capable of
fitting the data, but also highlights some mismatches
between the model used by the optimiser and that
assumed by the user.",
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notes = "http://www.evostar.org/2021/ EvoApplications2021 held
in conjunction with EuroGP'2021, EvoCOP2021 and
EvoMusArt2021",
- }
Genetic Programming entries for
Neil Urquhart
Emma Hart
Silke Hoehl
Citations