Using genetic programming with negative parsimony pressure on exons for portfolio optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Nils Svangard and Peter Nordin and Stefan Lloyd",
  title =        "Using genetic programming with negative parsimony
                 pressure on exons for portfolio optimization",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1014--1017",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Adaptive
                 systems, Bioinformatics, Genetic mutations, Genomics,
                 Portfolios, Robustness, Testing, Training data,
                 genetics, problem solving, Occam Razor principle,
                 exons, financial portfolio optimisation problem,
                 parsimony pressure",
  ISBN =         "0-7803-7804-0",
  DOI =          "doi:10.1109/CEC.2003.1299778",
  abstract =     "Traditionally Parsimony Pressure has been used with
                 Genetic Programming to reduce the complexity of
                 solutions analogous to the principle of Occam's Razor.
                 But there have been several signs from previous
                 experiments that this reduces the quality of the
                 solutions. In an attempt to counteract this we presents
                 one of the first experiments that try to apply negative
                 parsimony pressure on genetic programming, ie. we
                 prefer complex solutions rather than simpler ones. This
                 system is then applied on a financial portfolio
                 optimisation problem to test it's performance on real
                 world data. Our results indicate that negative
                 parsimony pressure work better than regular parsimony
                 pressure on average, and it's almost always better to
                 use some kind of parsimony pressure than not.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Nils Svangard Peter Nordin Stefan Lloyd