Experimental Investigations into Graph Grammar Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@PhdThesis{Luerssen2006,
  author =       "Martin Holger Luerssen",
  title =        "Experimental Investigations into Graph Grammar
                 Evolution",
  school =       "School of Informatics and Engineering, The Flinders
                 University of South Australia",
  year =         "2006",
  type =         "PhD",
  address =      "Adelaide, Australia",
  month =        may # " 29",
  keywords =     "genetic algorithms, genetic programming, embryogeny",
  URL =          "http://theses.flinders.edu.au/uploads/approved/adt-SFU20110328.120915/public/02whole.pdf",
  URL =          "http://theses.flinders.edu.au/public/adt-SFU20110328.120915/index.html",
  size =         "224 pages",
  abstract =     "Artificial and natural instances of networks are
                 ubiquitous, and many problems of practical interest may
                 be formulated as questions about networks. Determining
                 the optimal topology of a network is pertinent to many
                 domains. Evolutionary algorithms constitute a
                 well-established optimisation method, but they scale
                 poorly if applied to the combinatorial explosion of
                 possible network topologies. Generative representation
                 schemes aim to overcome this by facilitating the
                 discovery and reuse of design dependencies and allowing
                 for adaptable exploration strategies. Biological
                 embryogenesis is a strong inspiration for many such
                 schemes, but the associated complexities of modelling
                 lead to impractical simulation times and poor
                 conceptual understanding. Existing research also
                 predominantly focuses on specific design domains such
                 as neural networks.

                 This thesis seeks to define a simple yet universally
                 applicable and scalable method for evolving graphs and
                 networks. A number of contributions are made in this
                 regard. We establish the notion of directly evolving a
                 graph grammar from which a population of networks can
                 be derived. Compact cellular productions that form a
                 hypergraph grammar are optimised by a novel
                 multi-objective evolutionary design system called
                 G/GRADE. A series of empirical investigations are then
                 carried out to gain a better understanding of graph
                 grammar evolution. G/GRADE is applied to four domains:
                 symbolic regression, circuit design, neural networks,
                 and telecommunications. We compare different strategies
                 for composing graphs from randomly mutated productions
                 and examine the relationship between graph grammar
                 diversity and fitness, presenting both the use of
                 phenotypic diversity objectives and an island model to
                 improve this. Additionally, we address the issue of
                 bloat and demonstrate how concepts from swarm
                 intelligence can be applied to production selection and
                 mutation to improve grammatical convergence. The
                 results of this thesis are relevant to evolutionary
                 research into networks and grammars, and the wide
                 applicability and potential of graph grammar evolution
                 is expected to inspire further study.",
  notes =        "http://csem.flinders.edu.au/research/papers/bibtex.html

                 http://www.flinders.edu.au/science_engineering/csem/publications/phd-theses.cfm",
}

Genetic Programming entries for Martin H Luerssen