Evolutionary Program Induction Directed by Logic Grammars

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

@Article{ManLeungWong:1997:epidlg,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Evolutionary Program Induction Directed by Logic
                 Grammars",
  journal =      "Evolutionary Computation",
  year =         "1997",
  volume =       "5",
  number =       "2",
  pages =        "143--180",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, logic grammars",
  URL =          "http://cptra.ln.edu.hk/~mlwong/journal/ec1997.pdf",
  URL =          "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1997.5.2.143",
  DOI =          "doi:10.1162/evco.1997.5.2.143",
  size =         "39 pages",
  abstract =     "Program induction generates a computer program that
                 can produce the desired behavior for a given set of
                 situations. Two of the approaches in program induction
                 are inductive logic programming (ILP) and genetic
                 programming (GP). Since their formalisms are so
                 different, these two approaches cannot be integrated
                 easily, although they share many common goals and
                 functionalities. A unification will greatly enhance
                 their problem-solving power. Moreover, they are
                 restricted in the computer languages in which programs
                 can be induced. In this paper, we present a flexible
                 system called LOGENPRO (The LOgic grammar-based GENetic
                 PROgramming system) that uses some of the techniques of
                 GP and ILP. It is based on a formalism of logic
                 grammars. The system applies logic grammars to control
                 the evolution of programs in various programming
                 languages and represent context-sensitive information
                 and domain-dependent knowledge. Experiments have been
                 performed to demonstrate that LOGENPRO can emulate GP
                 and GP with automatically defined functions (ADFs).
                 Moreover, LOGENPRO can employ knowledge such as
                 argument types in a unified framework. The experiments
                 show that LOGENPRO has superior performance to that of
                 GP and GP with ADFs when more domain-dependent
                 knowledge is available. We have applied LOGENPRO to
                 evolve general recursive functions for the
                 even-n-parity from noisy training examples. A number of
                 experiments have been performed to determine the impact
                 of domain-specific knowledge and noise in training
                 examples on the speed of learning.",
  notes =        "Evolutionary Computation (Journal)

                 Special Issue: Trends in Evolutionary Methods for
                 Program Induction",
}

Genetic Programming entries for Man Leung Wong Kwong-Sak Leung

Citations