Generative Representations for Artificial Architecture and Passive Solar Performance

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Harrington:2013:CEC,
  article_id =   "1561",
  author =       "Adrian Harrington and Brian J. Ross",
  title =        "Generative Representations for Artificial Architecture
                 and Passive Solar Performance",
  booktitle =    "2013 IEEE Conference on Evolutionary Computation",
  volume =       "1",
  year =         "2013",
  month =        jun # " 20-23",
  editor =       "Luis Gerardo {de la Fraga}",
  pages =        "537--545",
  address =      "Cancun, Mexico",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4799-0453-2",
  DOI =          "doi:10.1109/CEC.2013.6557615",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.419.3502",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.419.3502",
  URL =          "http://www.cosc.brocku.ca/sites/all/files/downloads/research/cs1302.pdf",
  size =         "9 pages",
  abstract =     "This paper explores how the use of generative
                 representations influences the quality of solutions in
                 evolutionary design problems. A genetic programming
                 system is developed with individuals encoded as
                 generative representations. Two research goals motivate
                 this work. One goal is to examine Hornby's features and
                 measures of modularity, reuse and hierarchy in new and
                 more complex evolutionary design problems. In
                 particular, we consider a more difficult problem domain
                 where the generated 3D models are no longer constrained
                 by voxels. Experiments are carried out to generate 3D
                 models which grow towards a set of target points. The
                 results show that the generative representations with
                 the three features of modularity, regularity and
                 hierarchy performed best overall. Although the measures
                 of these features were largely consistent with those of
                 Hornby, a few differences were found. Our second
                 research goal is to use the best performing encoding on
                 some 3D modeling problems that involve passive solar
                 performance criteria. Here, the system is challenged
                 with generating forms that optimize exposure to the
                 Sun. This is complicated by the fact that a model's
                 structure can interfere with solar exposure to itself;
                 for example, protrusions can block Sun exposure to
                 other model elements. Furthermore, external
                 environmental factors (geographic location, time of the
                 day, time of the year, other buildings in the
                 proximity) may also be relevant. Experimental results
                 were successful, and the system was shown to scale well
                 to the architectural problems studied.",
  notes =        "also known as \cite{6557615}.

                 See also technical report CS-13-02 March 2013
                 cs1302.pdf

                 CEC 2013 - A joint meeting of the IEEE, the EPS and the
                 IET.",
}

Genetic Programming entries for Adrian Harrington Brian J Ross

Citations