Graph Grammar Encoding and Evolution of Automata Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Martin H. Luerssen",
  editor =       "Vladimir Estivill-Castro",
  title =        "Graph Grammar Encoding and Evolution of Automata
  booktitle =    "Twenty-Eighth Australasian Computer Science Conference
  series =       "CRPIT",
  volume =       "38",
  pages =        "229--238",
  publisher =    "ACS",
  address =      "Newcastle, Australia",
  year =         "2005",
  keywords =     "genetic algorithms, genetic programming, graph
                 grammars, neural networks",
  URL =          "",
  size =         "4 pages",
  abstract =     "The global dynamics of automata networks (such as
                 neural networks) are a function of their topology and
                 the choice of automata used. Evolutionary methods can
                 be applied to the optimisation of these parameters, but
                 their computational cost is prohibitive unless they
                 operate on a compact representation. Graph grammars
                 provide such a representation by allowing network
                 regularities to be efficiently captured and reused. We
                 present a system for encoding and evolving automata
                 networks as collective hypergraph grammars, and
                 demonstrate its efficacy on the classical problems of
                 symbolic regression and the design of neural network

Genetic Programming entries for Martin H Luerssen