Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules

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

@TechReport{becker:2003-09,
  author =       "Lee A. Becker and Mukund Seshadri",
  title =        "Comprehensibility and Overfitting Avoidance in Genetic
                 Programming for Technical Trading Rules",
  institution =  "Worcester Polytechnic Institute",
  year =         "2003",
  month =        may,
  email =        "mukund@cs.wpi.edu",
  keywords =     "genetic algorithms, genetic programming,
                 comprehensibility , Occam's razor, overfitting,
                 complexity penalising, S&P500, technical analysis,
                 market timing",
  URL =          "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-09.pdf",
  URL =          "http://citeseer.ist.psu.edu/574013.html",
  abstract =     "This paper presents two methods for increasing
                 comprehensibility in technical trading rules produced
                 by Genetic Programming. For this application domain
                 adding a complexity penalizing factor to the objective
                 fitness function also avoids overfitting the training
                 data. Using pre-computed derived technical indicators,
                 although it biases the search, can express complexity
                 while retaining comprehensibility. Several of the
                 learned technical trading rules outperform a buy and
                 hold strategy for the S&P500 on the testing period from
                 1990-2002, even taking into account transaction
                 costs.",
}

Genetic Programming entries for Lee A Becker Mukund Seshadri

Citations