Enhancing Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Harper:thesis,
  author =       "Robin Thomas Ross Harper",
  title =        "Enhancing Grammatical Evolution",
  school =       "School of Computer Science and Engineering, The
                 University of New South Wales",
  year =         "2009",
  address =      "Sydney 2052, Australia",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Dynamically Defined Functions, DDF, SCALP",
  URL =          "http://handle.unsw.edu.au/1959.4/44843",
  URL =          "http://unsworks.unsw.edu.au/fapi/datastream/unsworks:8140/SOURCE1.pdf",
  size =         "218 pages",
  abstract =     "Grammatical Evolution (GE) is a method of using a
                 general purpose evolutionary algorithm to evolve
                 programs written in an arbitrary BNF grammar. This
                 thesis extends GE as follows:

                 GE as an extension of Genetic Programming (GP)

                 A novel method of automatically extracting information
                 from the grammar is introduced. This additional
                 information allows the use of GP style crossover which
                 in turn allows GE to perform identically to a strongly
                 typed GP system as well as a non-typed (or canonical)
                 GP system. Two test problems are presented one which is
                 more easily solved by the GP style crossover and one
                 which favours the tradition GE Ripple Crossover. With
                 this new crossover operator GE can now emulate GP (as
                 well as retaining its own unique features) and can
                 therefore now be seen as an extension of
                 GP.

                 Dynamically Defined Functions

                 An extension to the BNF grammar is presented which
                 allows the use of dynamically defined functions (DDFs).
                 DDFs provide an alternative to the traditional approach
                 of Automatically Defined Functions (ADFs) but have the
                 advantage that the number of functions and their
                 parameters do not need to be specified by the user in
                 advance. In addition DDFs allow the architecture of
                 individuals to change dynamically throughout the course
                 of the run without requiring the introduction of any
                 new form of operator. Experimental results are
                 presented confirming the effectiveness of
                 DDFs.

                 Self-Selecting (or Variable) Crossover.

                 A self-selecting operator is introduced which allows
                 the system to determine, during the course of the run,
                 which crossover operator to apply; this is tested over
                 several problem domains and (especially where small
                 populations are used) is shown to be effective in
                 aiding the system to overcome local optima.

                 Spatial Co-Evolution in Age Layered Planes (SCALP)

                 A method of combining Hornby's ALPS metaheuristic and
                 the spatial co-evolution system introduced by Mitchell
                 is presented; the new SCALP system is tested over three
                 problem domains of increasing difficulty and performs
                 extremely well in each of them.",
  notes =        "Supervisor: Alan Blair",
}

Genetic Programming entries for Robin Harper

Citations