Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@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

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