Promoting the generalisation of genetically induced trading rules

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

@InProceedings{agapitosetal:2010:cfe,
  author =       "Alexandros Agapitos and Michael O'Neill and 
                 Anthony Brabazon",
  title =        "Promoting the generalisation of genetically induced
                 trading rules",
  booktitle =    "Proceedings of the 4th International Conference on
                 Computational and Financial Econometrics CFE'10",
  year =         "2010",
  editor =       "G. Kapetanios and O. Linton and M. McAleer and 
                 E. Ruiz",
  pages =        "E678",
  address =      "Senate House, University of London, UK",
  month =        "10-12 " # dec,
  organisation = "CSDA, LSE, Queen Mary and Westerfield College",
  publisher =    "ERCIM",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cfe-csda.org/cfe10/LondonBoA.pdf",
  size =         "Abstracts only",
  abstract =     "The goal of Machine Learning is not to induce an exact
                 representation of the training patterns themselves, but
                 rather to build a model of the underlying
                 pattern-generation process. One of the most important
                 aspects of this computational process is how to obtain
                 general models that are representative of the true
                 concept, and as a result, perform efficiently when
                 presented with novel patterns from that concept. A
                 particular form of evolutionary machine learning,
                 Genetic Programming, tackles learning problems by means
                 of an evolutionary process of program discovery. In
                 this paper we investigate the profitability of evolved
                 technical trading rules when accounting for the problem
                 of over-fitting. Out-of-sample rule performance
                 deterioration is a well-known problem, and has been
                 mainly attributed to the tendency of the evolved models
                 to find meaningless regularities in the training
                 dataset due to the high dimensionality of features and
                 the rich hypothesis space. We present a review of the
                 major established methods for promoting generalisation
                 in conventional machine learning paradigms. Then, we
                 report empirical results of adapting such techniques to
                 the Genetic Programming methodology, and applying it to
                 discover trading rules for various financial
                 datasets.",
  notes =        "http://www.cfe-csda.org/cfe10/",
}

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

Citations