Developing and evaluating incremental evolution using high quality performance measures for genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Walker:thesis,
  author =       "Matthew Garry William Walker",
  title =        "Developing and evaluating incremental evolution using
                 high quality performance measures for genetic
                 programming",
  school =       "Computer Science, Massey University",
  year =         "2007",
  type =         "Doctor of Philosophy",
  address =      "Albany, Auckland, New Zealand",
  keywords =     "genetic algorithms, genetic programming, Incremental
                 evolution, Performance measures",
  URL =          "http://hdl.handle.net/10179/738",
  URL =          "http://muir.massey.ac.nz/bitstream/10179/738/1/02whole.pdf",
  size =         "267 pages",
  abstract =     "This thesis is divided into two parts. The first part
                 considers and develops some of the statistics used in
                 genetic programming (GP) while the second uses those
                 statistics to study and develop a form of incremental
                 evolution and an early termination heuristic for
                 GP.

                 The first part looks in detail at success proportion,
                 Koza's minimum computational effort, and a measure we
                 rename {"}success effort{"}. We describe and develop
                 methods to produce confidence intervals for these
                 measures as well as confidence intervals for the
                 difference and ratio of these measures.

                 The second part studies Jackson's fitness-based
                 incremental evolution. If the number of fitness
                 evaluations are considered (rather than the number of
                 generations) then we find some potential benefit
                 through reduction in the effort required to find a
                 solution. We then automate the incremental evolution
                 method and show a statistically significant improvement
                 compared to GP with automatically defined functions
                 (ADFs).

                 The success effort measure is shown to have the
                 critical advantage over Koza's measure as it has the
                 ability to include a decreasing cost of failure. We
                 capitalise on this advantage by demonstrating an early
                 termination heuristic that again offers a statistically
                 significant advantage.",
  notes =        "Open BEAGLE

                 Supervisors: Chris Messom and Martin Johnson",
}

Genetic Programming entries for Matthew Walker

Citations