Model-driven regularization approach to straight line program genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Jose L. Montana and Cesar L. Alonso and 
                 Cruz E. Borges and Cristina Tirnauca",
  title =        "Model-driven regularization approach to straight line
                 program genetic programming",
  journal =      "Expert Systems with Applications",
  volume =       "57",
  pages =        "76--90",
  year =         "2016",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2016.03.003",
  URL =          "",
  abstract =     "This paper presents a regularization method for
                 program complexity control of linear genetic
                 programming tuned for transcendental elementary
                 functions. Our goal is to improve the performance of
                 evolutionary methods when solving symbolic regression
                 tasks involving Pfaffian functions such as polynomials,
                 analytic algebraic and transcendental operations like
                 sigmoid, inverse trigonometric and radial basis
                 functions. We propose the use of straight line programs
                 as the underlying structure for representing symbolic
                 expressions. Our main result is a sharp upper bound for
                 the Vapnik Chervonenkis dimension of families of
                 straight line programs containing transcendental
                 elementary functions. This bound leads to a
                 penalization criterion for the mean square error based
                 fitness function often used in genetic programming for
                 solving inductive learning problems. Our experiments
                 show that the new fitness function gives very good
                 results when compared with classical statistical
                 regularization methods (such as Akaike and Bayesian
                 Information Criteria) in almost all studied situations,
                 including some benchmark real-world regression
  keywords =     "genetic algorithms, genetic programming, Straight line
                 program, Pfaffian operator, Symbolic regression",

Genetic Programming entries for Jose Luis Montana Arnaiz Cesar Luis Alonso Cruz Enrique Borges Cristina Tirnauca