Created by W.Langdon from gp-bibliography.bib Revision:1.4771
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.",
Runner up 2006 Distinguished Dissertations http://www.bcs.org/server.php?show=conWebDoc.10343
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