Graph Design by Graph Grammar Evolution

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

@InProceedings{Luerssen:2007:cec,
  author =       "Martin H. Luerssen and David M. W. Powers",
  title =        "Graph Design by Graph Grammar Evolution",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "386--393",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1348.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424497",
  abstract =     "Determining the optimal topology of a graph is
                 pertinent to many domains, as graphs can be used to
                 model a variety of systems. Evolutionary algorithms
                 constitute a popular optimisation method, but
                 scalability is a concern with larger graph designs.
                 Generative representation schemes, often inspired by
                 biological development, seek to address this by
                 facilitating the discovery and reuse of design
                 dependencies and allowing for adaptable exploration
                 strategies. We present a novel developmental method for
                 optimising graphs that is based on the notion of
                 directly evolving a hypergraph grammar from which a
                 population of graphs can be derived. A multi-objective
                 design system is established and evaluated on problems
                 from three domains: symbolic regression, circuit
                 design, and neural control. The observed performance
                 compares favourably with existing methods, and
                 extensive reuse of subgraphs contributes to the
                 efficient representation of solutions. Constraints can
                 also be placed on the type of explored graph spaces,
                 ranging from tree to pseudograph. We show that more
                 compact solutions are attainable in less constrained
                 spaces, although convergence typically improves with
                 more constrained designs.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C

                 is it a GP? Evolution of executable grammar?",
}

Genetic Programming entries for Martin H Luerssen David M W Powers

Citations