Learning monitoring strategies: A difficult genetic programming application

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

@InProceedings{Atkin:1994:LMSDGP,
  author =       "Marc S. Atkin and Paul R. Cohen",
  title =        "Learning monitoring strategies: A difficult genetic
                 programming application",
  booktitle =    "Proceedings of the 1994 IEEE World Congress on
                 Computational Intelligence",
  year =         "1994",
  volume =       "1",
  pages =        "328--332a",
  address =      "Orlando, Florida, USA",
  month =        "27-29 " # jun,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, cupcake
                 problem, agent control language, genetic programming
                 application, monitoring strategy learning, optimal
                 strategies, possible behaviour, learning (artificial
                 intelligence), monitoring; optimisation",
  URL =          "http://www-eksl.cs.umass.edu/papers/AtkinIEEE.pdf",
  URL =          "http://citeseer.ist.psu.edu/94049.html",
  DOI =          "doi:10.1109/ICEC.1994.349931",
  size =         "6 pages",
  abstract =     "Finding optimal or at least good monitoring strategies
                 is an important consideration when designing an agent.
                 We have applied genetic programming to this task, with
                 mixed results. Since the agent control language was
                 kept purposefully general, the set of monitoring
                 strategies constitutes only a small part of the overall
                 space of possible behaviours. Because of this, it was
                 often difficult for the genetic algorithm to evolve
                 them, even though their performance was superior. These
                 results raise questions as to how easy it will be for
                 genetic programming to scale up as the areas it is
                 applied to become more complex.",
  notes =        "Novel? chrome/program structure linear, close to
                 assembly language, used GOTOs and interrupt handlers.
                 Did _not_ get performance improvement on changing to
                 parse trees. Did evolve progs to control agents which
                 moved to the goal without colliding with an obstacle.
                 Finally cautions about problems with GP scaling
                 up.

                 {"}Also tried local mating (also known as fine grain
                 parallelism){"}

                 Also available as Technical Report 94-52, Dept. of
                 Computer Science, University of Massachusetts/Amherst,
                 USA?",
}

Genetic Programming entries for Marc S Atkin Paul R Cohen

Citations