Policy Evolution with Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Yow Tzu Lim and Pau-Chen Cheng and 
                 John Andrew Clark and Pankaj Rohatgi",
  title =        "Policy Evolution with Grammatical Evolution",
  booktitle =    "Proceedings of the 7th International Conference on
                 Simulated Evolution And Learning (SEAL '08)",
  year =         "2008",
  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",
  volume =       "5361",
  series =       "Lecture Notes in Computer Science",
  pages =        "71--80",
  address =      "Melbourne, Australia",
  month =        dec # " 7-10",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  isbn13 =       "978-3-540-89693-7",
  DOI =          "doi:10.1007/978-3-540-89694-4_8",
  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.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",

Genetic Programming entries for Yow Tzu Lim Pau Chen Cheng John A Clark Pankaj Rohatgi