An Investigation into the Suitability of Genetic Programming for Computing Visibility Areas for Sensor Planning

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

@PhdThesis{grant:phd,
  author =       "Michael Sean Grant",
  title =        "An Investigation into the Suitability of Genetic
                 Programming for Computing Visibility Areas for Sensor
                 Planning",
  school =       "Department of Computing and Electrical Engineering,
                 Heriot-Watt University",
  year =         "2000",
  address =      "Riccarton, Edinburgh EH14 4AS, United Kingdom",
  month =        may,
  email =        "gp@michael-grant.me.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.michael-grant.me.uk/phd.zip",
  URL =          "http://hdl.handle.net/10399/555",
  URL =          "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325090",
  URL =          "http://books.google.co.uk/books?id=Mr4dHAAACAAJ",
  size =         "293 pages",
  abstract =     "This thesis considers the application of Genetic
                 Programming to visibility space calculation, for Sensor
                 Planning in Machine Vision. This is a problem
                 considerably more complex than most for which GP has
                 been used; no closed-form algorithm for it yet exists
                 in the most general case.

                 The main contributions and results are the application
                 of GP to a new field, and the conclusion that GP is
                 better suited to solve this complex problem by a
                 generate-and-test approach than an analytic one.

                 Three systems were implemented to evolve programs for
                 calculating visibility spaces. The first used untyped
                 GP and low-level operations, for maximum flexibility in
                 evolution, but could solve the problem only for trivial
                 cases.

                 The second used high-level geometric operations and
                 typed GP, but tended to get trapped in local optima.
                 Approaches used, unsuccessfully, to obviate this
                 included altering the fitness cases and function set
                 both statically and dynamically, parameter tuning,
                 seeding the population, using program templates, and
                 using a simpler system for modelling evolution.

                 The third system, which used a generate-and-test
                 approach, evolved useful solutions. When seeded with
                 hand-crafted partial solutions, it was able to improve
                 them considerably.

                 The work shows the potential of GP to evolve or refine
                 a region-growing generate-and-test algorithm for
                 calculating visibility spaces, a problem not hitherto
                 approached by the GP community.",
  notes =        "phd.zip is 3390593 bytes

                 uk.bl.ethos.325090",
}

Genetic Programming entries for Michael Sean Grant

Citations