Evolutionary Computation in Intelligent Network Management

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

  author =       "Ajith Abraham",
  title =        "Evolutionary Computation in Intelligent Network
  booktitle =    "Evolutionary Computing in Data Mining",
  publisher =    "Springer",
  year =         "2004",
  editor =       "Ashish Ghosh and Lakhmi C. Jain",
  volume =       "163",
  series =       "Studies in Fuzziness and Soft Computing",
  chapter =      "9",
  pages =        "189--210",
  keywords =     "genetic algorithms, genetic programming, Linear
                 Genetic Programming, LGP, intrusion detection, ANN,
                 www, fuzzy clustering, fuzzy inference, computer
                 security, RIPPER, demes (ring topology), steady state
                 32-bit FPU machine code GP, SVM, decision trees,
  ISBN =         "3-540-22370-3",
  URL =          "http://www.softcomputing.net/ec_web-chapter.pdf",
  URL =          "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html",
  abstract =     "Data mining is an iterative and interactive process
                 concerned with discovering patterns, associations and
                 periodicity in real world data. This chapter presents
                 two real world applications where evolutionary
                 computation has been used to solve network management
                 problems. First, we investigate the suitability of
                 linear genetic programming (LGP) technique to model
                 fast and efficient intrusion detection systems, while
                 comparing its performance with artificial neural
                 networks and classification and regression trees.
                 Second, we use evolutionary algorithms for a Web
                 usage-mining problem. Web usage mining attempts to
                 discover useful knowledge from the secondary data
                 obtained from the interactions of the users with the
                 Web. Evolutionary algorithm is used to optimise the
                 concurrent architecture of a fuzzy clustering algorithm
                 (to discover data clusters) and a fuzzy inference
                 system to analyse the trends. Empirical results clearly
                 shows that evolutionary algorithm could play a major
                 rule for the problems considered and hence an important
                 data mining tool.",
  size =         "22 pages",

Genetic Programming entries for Ajith Abraham