Policy Evolution with Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{DBLP:conf/seal/LimCCR08,
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author = "Yow Tzu Lim and Pau-Chen Cheng and
John Andrew Clark and Pankaj Rohatgi",
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title = "Policy Evolution with Grammatical Evolution",
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booktitle = "Proceedings of the 7th International Conference on
Simulated Evolution And Learning (SEAL '08)",
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year = "2008",
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editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and
David G. Green and Victor Ciesielski and
Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and
Kalyanmoy Deb and Kay Chen Tan and
J{\"u}rgen Branke and Yuhui Shi",
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volume = "5361",
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series = "Lecture Notes in Computer Science",
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pages = "71--80",
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address = "Melbourne, Australia",
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month = dec # " 7-10",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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isbn13 = "978-3-540-89693-7",
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DOI = "doi:10.1007/978-3-540-89694-4_8",
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abstract = "Security policies are becoming more sophisticated.
Operational forces will often be faced with making
tricky risk decisions and policies must be flexible
enough to allow appropriate actions to be facilitated.
Access requests are no longer simple subject access
object matters. There is often a great deal of context
to be taken into account. Most security work is couched
in terms of risk management, but the benefits of
actions will need to be taken into account too. In some
cases it may not be clear what the policy should be.
People are often better at dealing with specific
examples than producing general rules. In this paper we
investigate the use of Grammatical Evolution (GE) to
attempt to infer Fuzzy MLS policy from decision
examples. This approach couches policy inference as a
search for a policy that is most consistent with the
supplied examples set. The results show this approach
is promising.",
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bibsource = "DBLP, http://dblp.uni-trier.de",
- }
Genetic Programming entries for
Yow Tzu Lim
Pau Chen Cheng
John A Clark
Pankaj Rohatgi
Citations