Multiple Solutions by Means of Genetic Programming: A Collision Avoidance Example

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Daniel Howard",
  title =        "Multiple Solutions by Means of Genetic Programming:
                 {A} Collision Avoidance Example",
  booktitle =    "Proceedings of the Second International Conference on
                 Rough Sets and Knowledge Technology, RSKT 2007",
  year =         "2007",
  editor =       "Jingtao Yao and Pawan Lingras and Wei-Zhi Wu and 
                 Marcin S. Szczuka and Nick Cercone and Dominik Slezak",
  volume =       "4481",
  series =       "Lecture Notes in Computer Science",
  pages =        "508--517",
  address =      "Toronto, Canada",
  month =        may # " 14-16",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Multiple
  isbn13 =       "978-3-540-72457-5",
  DOI =          "doi:10.1007/978-3-540-72458-2_63",
  size =         "10 pages",
  abstract =     "Seldom is it practical to completely automate the
                 discovery of the Pareto Frontier by genetic programming
                 (GP). It is not only difficult to identify all of the
                 optimization parameters a-priori but it is hard to
                 construct functions that properly evaluate parameters.
                 For instance, the ease of manufacture of a particular
                 antenna can be determined but coming up with a function
                 to judge this on all manner of GP-discovered antenna
                 designs is impractical. This suggests using GP to
                 discover many diverse solutions at a particular point
                 in the space of requirements that are quantifiable,
                 only a-posteriori (after the run) to manually test how
                 each solution fares over the less tangible requirements
                 e.g. ease of manufacture. Multiple solutions can also
                 suggest requirements that are missing. A new toy
                 problem involving collision avoidance is introduced to
                 research how GP may discover a diverse set of multiple
                 solutions to a single problem. It illustrates how
                 emergent concepts (linguistic labels) rather than
                 distance measures can cluster the GP generated multiple
                 solutions for their meaningful separation and
  notes =        "railway track, two train speeds, GP sets the points",
  bibdate =      "2007-07-05",
  bibsource =    "DBLP,

Genetic Programming entries for Daniel Howard