Promoting the generalisation of genetically induced trading rules

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@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