Geometric Semantic Genetic Programming for Financial Data

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

@InProceedings{mcdermott:evoapps14,
  author =       "James McDermott and Alexandros Agapitos and 
                 Anthony Brabazon and Michael O'Neill",
  title =        "Geometric Semantic Genetic Programming for Financial
                 Data",
  booktitle =    "17th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2014",
  editor =       "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora",
  series =       "LNCS",
  volume =       "8602",
  publisher =    "Springer",
  pages =        "215--226",
  address =      "Granada",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Automated
                 trading, Commodity, Exchange rate, Index, Semantics,
                 Fitness landscape, Hill-climbing",
  isbn13 =       "978-3-662-45522-7",
  DOI =          "doi:10.1007/978-3-662-45523-4",
  size =         "12 pages",
  abstract =     "We cast financial trading as a symbolic regression
                 problem on the lagged time series, and test a state of
                 the art symbolic regression method on it. The system is
                 geometric semantic genetic programming, which achieves
                 good performance by converting the fitness landscape to
                 a cone landscape which can be searched by
                 hill-climbing. Two novel variants are introduced and
                 tested also, as well as a standard hill-climbing
                 genetic programming method. Baselines are provided by
                 buy-and-hold and ARIMA. Results are promising for the
                 novel methods, which produce smaller trees than the
                 existing geometric semantic method. Results are also
                 surprisingly good for standard genetic programming. New
                 insights into the behaviour of geometric semantic
                 genetic programming are also generated.",
  affiliation =  "Financial Mathematics and Computation Research Cluster
                 Natural Computing Research and Applications Group
                 Complex and Adaptive Systems Laboratory, University
                 College Dublin, Ireland",
  notes =        "evoFIN EvoApplications2014 held in conjunction with
                 EuroGP'2014, EvoCOP2014, EvoBIO2014, and
                 EvoMusArt2014",
}

Genetic Programming entries for James McDermott Alexandros Agapitos Anthony Brabazon Michael O'Neill

Citations