An Analysis of the Impact of Functional Programming Techniques on Genetic Programming

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

@PhdThesis{TinaYu:thesis,
  author =       "Gwoing Tina Yu",
  title =        "An Analysis of the Impact of Functional Programming
                 Techniques on Genetic Programming",
  school =       "University College, London",
  year =         "1999",
  address =      "Gower Street, London, WC1E 6BT",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Thesis.pdf",
  URL =          "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Thesis.ps.gz",
  URL =          "ftp://bells.cs.ucl.ac.uk/functional/papers/tina_yu_thesis.pdf.gz",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/tinayu/TinaYuThesis.ps.gz",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=19&uin=uk.bl.ethos.313519",
  size =         "185 pages",
  abstract =     "Genetic Programming (GP) automatically generates
                 computer programs to solve specified problems. It
                 develops programs through the process of a
                 {"}create-test-modify{"} cycle which is similar to the
                 way a human writes programs. There are various
                 functional programming techniques that human
                 programmers can use to accelerate the program
                 development process. This research investigated the
                 applicability of some of the functional techniques to
                 GP and analyzed their impact on GP performance.

                 Among many important functional techniques, three were
                 chosen to be included in this research, due to their
                 relevance to GP. They are polymorphism, implicit
                 recursion and higher-order functions. To demonstrate
                 their applicability, a GP system was developed with
                 those techniques incorporated. Furthermore, a number of
                 experiments were conducted using the system. The
                 results were then compared to those generated by other
                 GP systems which do not support these functional
                 features. Finally, the program search space of the
                 general even-parity problem was analyzed to explain how
                 these techniques impact GP performance.

                 The experimental results showed that the investigated
                 functional techniques have made GP more powerful in the
                 following ways: 1) polymorphism has enabled GP to solve
                 problems that are very difficult for standard GP to
                 solve, i.e. nth and map programs; 2) higher-order
                 functions and implicit recursion have enhanced GP's
                 ability in solving the general even-parity problem to a
                 greater degree than with any other known methods.
                 Moreover, the analysis showed that these techniques
                 directed GP to generate program solutions in a way that
                 has never been previously reported. Finally, we provide
                 the guidelines for the application of these techniques
                 to other problems.",
  notes =        "My version of ghostview barfs 8 March 2000 but
                 Thesis.ps prints ok uk.bl.ethos.313519 UCL
                 internal:000901353",
}

Genetic Programming entries for Tina Yu

Citations