Evolving Automata Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@MastersThesis{naidoo:masters,
  author =       "Amashini Naidoo",
  title =        "Evolving Automata Using Genetic Programming",
  school =       "School of Computer Science, University of
                 KwaZulu-Natal",
  year =         "2008",
  address =      "Durban, South Africa",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/naidoo_masters.pdf",
  size =         "306 pages",
  abstract =     "Automata have played a significant role in the field
                 of computer science. The idea of automatically inducing
                 automata has been the object of the computer science
                 community for a number of years. Genetic programming
                 (GP) is an evolutionary algorithm modelled on the
                 Darwinian idea of natural selection and genetic
                 recombination, where individuals are typically
                 represented as tree-structures.

                 This thesis investigates genetic programming as a
                 method to automatically evolve various classes of
                 automata including finite acceptors, pushdown automata,
                 finite state transducers and Turing machines for
                 benchmark sets of problems. A new approach to evolving
                 automata is introduced whereby each individual in the
                 GP population is represented directly as a graph as
                 opposed to a tree. The methods for evaluation, standard
                 and advanced GP characteristics, and the GP parameters
                 are identified.

                 Genetic programming proves to be an effective method
                 for inducing automata. The GP systems presented in this
                 thesis successfully induce solutions for finite
                 acceptors; pushdown automata, finite state transducer
                 and Turing machine languages. Furthermore, it is shown
                 that using non-destructive operators and multiple
                 iterations improve the success rate of the GP system.",
  notes =        "Supervisor: Dr. Nelishia Pillay",
}

Genetic Programming entries for Amashini Naidoo

Citations