Evolving Turing Machines from Examples

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

  author =       "Julio Tanomaru",
  title =        "Evolving {Turing} Machines from Examples",
  booktitle =    "Artificial Evolution",
  year =         "1993",
  editor =       "J.-K. Hao and E. Lutton and E. Ronald and 
                 M. Schoenauer and D. Snyers",
  volume =       "1363",
  series =       "LNCS",
  pages =        "167--180",
  address =      "Nimes, France",
  month =        oct,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/BFb0026599",
  size =         "14 pages",
  abstract =     "The aim of this paper is to investigate the
                 application of evolutionary approaches to the automatic
                 design of automata in general, and Turing machines, in
                 particular. Here, each automaton is represented
                 directly by its state transition table and the number
                 of states is allowed to change dynamically as evolution
                 takes place. This approach contrasts with less natural
                 representation methods such as trees of genetic
                 programming, and allows for easier visualization and
                 hardware implementation of the obtained automata. Two
                 methods are proposed, namely, a straightforward,
                 genetic-algorithm-like one, and a more sophisticated
                 approach involving several operators and the 1/5 rule
                 of evolution strategy. Experiments were carried out for
                 the automatic generation of Turing machines from
                 examples of input and output tapes for problems of
                 sorting, unary arithmetic, and language acceptance, and
                 the results indicate the feasibility of the
                 evolutionary approach. Since Turing machines can be
                 viewed as general representations of computer programs,
                 the proposed approach can be thought of as a step
                 towards the generation of programs and algorithms by
  notes =        "AE'97",

Genetic Programming entries for Julio Tanomaru