Enhancement of Model Generalisation in Multiobjective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Ji Ni",
  title =        "Enhancement of Model Generalisation in Multiobjective
                 Genetic Programming",
  school =       "Electronic and Electrical Engineering, University of
  year =         "2013",
  address =      "UK",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, MOGP",
  URL =          "http://etheses.whiterose.ac.uk/5021/1/jnThesis_1.0.5.pdf",
  URL =          "http://etheses.whiterose.ac.uk/5021/1/jnThesis_1.0.5.docx",
  URL =          "http://etheses.whiterose.ac.uk/5021/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=32&uin=uk.bl.ethos.589326",
  size =         "133 pages",
  abstract =     "Multi-objective genetic programming (MOGP) is a
                 powerful evolutionary algorithm that requires no human
                 pre-fixed model sets to handle regression and
                 classification problems in the machine learning area.
                 We aim to improve the model generalisation of MOGP in
                 both regression and classification tasks. The work in
                 this thesis has three main contributions. First, we
                 propose replacing the division operator used in genetic
                 programming with an analytic quotient (AQ) operator in
                 regression to systematically achieve lower mean squared
                 error due principally to removing the discontinuities
                 or singularities caused by conventional protected or
                 unprotected division. Further, this AQ operator is
                 differentiable. Second, we propose using Tikhonov
                 regularisation, in conjunction with node count (using
                 an extension of Pareto comparison from vectors to
                 tuples) as a general complexity measure in MOGP. We
                 demonstrate that employing this general complexity
                 yields mean squared test error measures over a range of
                 regression problems which are typically superior to
                 those from conventional node count. We further analysed
                 the reason why our new method outperforms the
                 conventional complexity measure and conclude that it
                 forms a decision mechanism which balances both
                 syntactic and semantic information. Third, we propose
                 using a loss measure complementary to Vapnik's
                 statistical learning theory, which can effectively
                 stabilise classifiers trained by MOGP. We demonstrate
                 that this loss measure has a number of attractive
                 properties and has a better correlation with
                 generalisation error compared to 0/1 loss, so that
                 better generalisation performance is achievable.",
  notes =        "Supervisor: Dr. Peter I. Rockett


Genetic Programming entries for Ji Ni