Policy Evolution with Genetic Programming: A Comparison of Three Approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Yow Tzu Lim and Pau Chen Cheng and 
                 John Andrew Clark and Pankaj Rohatgi",
  title =        "Policy Evolution with Genetic Programming: A
                 Comparison of Three Approaches",
  booktitle =    "2008 IEEE World Congress on Computational
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "1792--1800",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0442.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631032",
  abstract =     "In the early days a policy was a set of simple rules
                 with a clear intuitive motivation that could be
                 formalised to good effect. However the world is now
                 much more complex. Subtle risk decisions may often need
                 to be made and people are not always adept at
                 expressing rationale for what they do. Previous
                 research has demonstrated that Genetic Programming can
                 be used to infer statements of policies from examples
                 of decisions made [1]. This allows a policy that may
                 not formally have been documented to be discovered
                 automatically, or an underlying set of requirements to
                 be extracted by interpreting user decisions to posed
                 ``what if'' scenarios. This study compares the
                 performance of three different approaches in using
                 Genetic Programming to infer security policies from
                 decision examples made, namely symbolic regression,
                 IF-THEN rules inference and fuzzy membership functions
                 inference. The fuzzy membership functions inference
                 approach is found to have the best performance in terms
                 of accuracy. Also, the fuzzification and
                 de-fuzzification methods are found to be strongly
                 correlated; incompatibility between them can have
                 strong negative impact to the performance.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",

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