Genetic Programming for the Acquisition of Double Auction Market Strategies

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

@InCollection{kinnear:andrews,
  author =       "Martin Andrews and Richard Prager",
  title =        "Genetic Programming for the Acquisition of Double
                 Auction Market Strategies",
  booktitle =    "Advances in Genetic Programming",
  publisher =    "MIT Press",
  editor =       "Kenneth E. {Kinnear, Jr.}",
  year =         "1994",
  chapter =      "16",
  size =         "14 pages",
  keywords =     "genetic algorithms, genetic programming, SA",
  pages =        "355--368",
  URL =          "http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap16.pdf",
  abstract =     "The Double Auction (DA) is the mechanism behind the
                 minute-by-minute trading on many futures and commodity
                 exchanges. Since 1990, DA tournaments have been held by
                 the Santa Fe Institute. The competitors in the
                 tournaments are strategies embodied in computer
                 programmes written by a variety of economists, computer
                 scientists and mathematicians. This paper describes how
                 Genetic Programming (GP) methods have been used to
                 create strategies superior, in local DA playoffs, to
                 many of the hand-coded strategies.

                 To isolate the contribution that the evolutionary
                 process makes to the search for good strategies, we
                 compare GP and Simulated Annealing (SA) optimisation of
                 programmes. To reduce the cost of learning, we also
                 investigate an approach that uses statistical measures
                 to maintain a uniform population pressure.",
  notes =        "{"} a GP approach was very successful in learning
                 strategies for playing a simple game with complex
                 dynamics{"} Ref Knobeln Contest:
                 Sanfrancisco.ira.uka.de [129.13.13.110]
                 /pub/knobeln

                 Generational GP pop=300, touranment selection? size=2?
                 Comparison with Simulated Annealing:SA also good but GP
                 better Best GP exceeded performance of handcode
                 routines (on average?) 65% of time. Check details of
                 what exctly this means.

                 Set number of games played so could distinquish meadian
                 from top quartile with 95% confidence. Claims it helps,
                 but doesnt seem to have either speeded things at lot or
                 made much better result.

                 ",
}

Genetic Programming entries for Martin Andrews Richard Prager

Citations