Generative Representations for Evolving Families of Designs

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Gregory S. Hornby",
  title =        "Generative Representations for Evolving Families of
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1678--1689",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, parametric
                 Lindenmayer systems, evolving neural networks, ANN",
  URL =          "",
  broken =       "",
  DOI =          "doi:10.1007/3-540-45110-2_61",
  abstract =     "Since typical evolutionary design systems encode only
                 a single artifact with each individual, each time the
                 objective changes a new set of individuals must be
                 evolved. When this objective varies in a way that can
                 be parameterized, a more general method is to use a
                 representation in which a single individual encodes an
                 entire class of artifacts. In addition to saving time
                 by preventing the need for multiple evolutionary runs,
                 the evolution of parameter-controlled designs can
                 create families of artifacts with the same style and a
                 reuse of parts between members of the family. In this
                 paper an evolutionary design system is described which
                 uses a generative representation to encode families of
                 designs. Because a generative representation is an
                 algorithmic encoding of a design, its input parameters
                 are a way to control aspects of the design it
                 generates. By evaluating individuals multiple times
                 with different input parameters the evolutionary design
                 system creates individuals in which the input parameter
                 controls specific aspects of a design. This system is
                 demonstrated on two design substrates: neural-networks
                 which solve the 3/5/7-parity problem and
                 three-dimensional tables of varying heights.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",

Genetic Programming entries for Gregory S Hornby