Exploiting Development to Enhance the Scalability of Hardware Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Timothy Glennie Wilson Gordon",
  title =        "Exploiting Development to Enhance the Scalability of
                 Hardware Evolution",
  school =       "University College, London",
  year =         "2005",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, EHW",
  URL =          "http://www.bcs.org/upload/pdf/tgordon.pdf",
  size =         "302 pages",
  abstract =     "Evolutionary algorithms do not scale well to the
                 large, complex circuit design problems typical of the
                 real world. Although techniques based on traditional
                 design decomposition have been proposed to enhance
                 hardware evolution's scalability, they often rely on
                 traditional domain knowledge that may not be
                 appropriate for evolutionary search and might limit
                 evolution's opportunity to innovate.

                 It has been proposed that reliance on such knowledge
                 can be avoided by introducing a model of biological
                 development to the evolutionary algorithm, but this
                 approach has not yet achieved its potential. Prior
                 demonstrations of how development can enhance
                 scalability used toy problems that are not indicative
                 of evolving hardware. Prior attempts to apply
                 development to hardware evolution have rarely been
                 successful and have never explored its effect on
                 scalability in detail.

                 This thesis demonstrates that development can enhance
                 scalability in hardware evolution, primarily through a
                 statistical comparison of hardware evolution's
                 performance with and without development using circuit
                 design problems of various sizes. This is reinforced by
                 proposing and demonstrating three key mechanisms that
                 development uses to enhance scalability: the creation
                 of modules, the reuse of modules, and the discovery of
                 design abstractions.

                 The thesis includes several minor contributions:
                 hardware is evolved using a common reconfigurable
                 architecture at a lower level of abstraction than
                 reported elsewhere. It is shown that this can allow
                 evolution to exploit the architecture more efficiently
                 and perhaps search more effectively.

                 Also the benefits of several features of developmental
                 models are explored through the biases they impose on
                 the evolutionary search. Features that are explored
                 include the type of environmental context development
                 uses and the constraints on symmetry and information
                 transmission they impose, genetic operators that may
                 improve the robustness of gene networks, and how
                 development is mapped to hardware. Also performance is
                 compared against contemporary developmental models.",
  notes =        "Evolvable hardware rather than GP

                 Runner up 2006 Distinguished Dissertations

                 Exploiting Development to Enhance the Scalability of
                 Hardware Evolution Tim Gordon University College London
                 Supervised by Peter Rounce

                 Timothy Gordon received the B.Sc. in Chemistry, the
                 M.Sc. in Information Technology and the Ph.D. in
                 Computer Science from University College London in
                 1994, 1995 and 2005 respectively.

                 His Ph.D. research focussed on the application of
                 evolutionary algorithms and computational development
                 to hardware design. His recent interests include the
                 use of evolutionary algorithms in finance. He currently
                 works for a London hedge fund.",

Genetic Programming entries for Tim Gordon