Rapid Training of Thermal Agents with Single Parent Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Daniel A. Ashlock and Kenneth M. Bryden and 
                 Wendy Ashlock and Stephen P. Gent",
  title =        "Rapid Training of Thermal Agents with Single Parent
                 Genetic Programming",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "3",
  pages =        "2122--2129",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554957",
  abstract =     "The temperature profile across an object can be
                 computed by iterative methods. The time spent waiting
                 for iterative solutions to converge for multiple
                 objects in a complex configuration is an impediment to
                 exploratory analysis of engineering systems. A
                 high-quality rapidly computed initial guess can speed
                 convergence for an iterative algorithm. A system is
                 described and tested for creating thermal agents that
                 supply such initial guesses. Thermal agents are
                 specific to an object but general across different
                 thermal boundary conditions. During an off-line
                 training phase, genetic programming is used to locate a
                 thermal agent by training on several sets of boundary
                 conditions. In use, thermal agents transform boundary
                 conditions into rapidly-converged initial values on a
                 cellular decomposition of an object. the impact of
                 using single parent genetic programming on thermal
                 agents is tested. Single parent genetic programming
                 replaces the usual sub-tree crossover in genetic
                 programming with crossover with members of an
                 unchanging ancestor set. The use of this ancestor set
                 permits the incorporation of expert knowledge into the
                 system as well as permitting the re-use of solutions
                 derived on one object to speed training of thermal
                 agents for another object. For three types of
                 experiments, incorporating expert knowledge; re-using
                 evolved solutions; and transferring knowledge between
                 distinct configurations statistically significant
                 improvements are obtained with single parent
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Daniel Ashlock Kenneth M Bryden Wendy Ashlock Stephen P Gent