Applying Logic Grammars to Induce Sub-Functions in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Applying Logic Grammars to Induce Sub-Functions in
                 Genetic Programming",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "737--740",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, LOGENPRO",
  URL =          "",
  abstract =     "Genetic Programming (GP) is a method of automatically
                 inducing S-expression in LISP to perform specified
                 tasks. The problem of inducing programs can be
                 reformulated as a search for a highly fit program in
                 the space of all possible programs. This paper presents
                 a framework in which the search space can be specified
                 declaratively by a user. Its application in inducing
                 sub-functions is detailed. The framework is based on a
                 formalism of logic grammars and it is implemented as a
                 system called LOGENPRO (the LOgic grammar based GENetic
                 PROgramming system). The formalism is powerful enough
                 to represent context-sensitive information and
                 domain-dependent knowledge. This knowledge can be used
                 to accelerate the learning speed and/or improve the
                 quality of the programs induced. The system is also
                 very flexible and programs in various programming
                 languages can be acquired.

                 Automatic discovery of sub-functions is one of the most
                 important research areas in Genetic Programming. An
                 experiment is used to demonstrate that LOGENPRO can
                 emulate Koza's Automatically Defined Functions (ADF).
                 Moreover, LOGENPRO can employ knowledge such as
                 argument types in a unified framework. The experiment
                 shows that LOGENPRO has superior performance to that of
                 Koza's ADF when more domain- dependent knowledge is
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 conference details at

Genetic Programming entries for Man Leung Wong Kwong-Sak Leung