Genetic programming and evolutionary generalization

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

  author =       "Ibrahim Kushchu",
  title =        "Genetic programming and evolutionary generalization",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2002",
  type =         "Paper",
  volume =       "6",
  number =       "5",
  month =        oct,
  pages =        "431--442",
  keywords =     "genetic algorithms, genetic programming,
                 generalisation, machine learning, robustness, data
                 sets, evolutionary generalisation, learning problems,
                 simulating learning behaviours, solution brittleness,
                 supervised learning, evolutionary computation,
                 generalisation (artificial intelligence), learning
                 (artificial intelligence)",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2002.805038",
  abstract =     "In genetic programming (GP), learning problems can be
                 classified broadly into two types: those using data
                 sets, as in supervised learning, and those using an
                 environment as a source of feedback. An increasing
                 amount of research has concentrated on the robustness
                 or generalisation ability of the programs evolved using
                 GP. While some of the researchers report on the
                 brittleness of the solutions evolved, others proposed
                 methods of promoting robustness/generalization. It is
                 important that these methods are not ad hoc and are
                 applicable to other experimental setups. In this paper,
                 learning concepts from traditional machine learning and
                 a brief review of research on generalisation in GP are
                 presented. The paper also identifies problems with
                 brittleness of solutions produced by GP and suggests a
                 method for promoting robustness/generalisation of the
                 solutions in simulating learning behaviours using GP.",
  notes =        "Graduate Sch. of Int. Manage., Int. Univ. of Japan,

                 PAC. Santa Fe Ant trail.",

Genetic Programming entries for Ibrahim Kuscu