Enhancements to a hybrid genetic programming technique applied to symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Umberto Armani and Vassili V. Toropov and 
                 Andrey Polynkin and Osvaldo M. Querin and Luis Alvarez",
  title =        "Enhancements to a hybrid genetic programming technique
                 applied to symbolic regression",
  booktitle =    "Proceedings of the 8th ASMO UK / ISSMO conference on
                 Engineering Design Optimization Product and Process
  year =         "2010",
  editor =       "Fabian Duddeck and Osvaldo M. Querin and 
                 Johann Sienz and Vassili V. Toropov and M. Hasan Shaheed",
  address =      "Queen Mary University of London, UK",
  month =        jul # " 8-9",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-85316-292-6",
  URL =          "http://www.asmo-uk.com/8th-asmo-uk/html/menu_page.html",
  URL =          "http://www.asmo-uk.com/8th-asmo-uk/presentations/session1_presentation5.pdf",
  abstract =     "A major problem in genetic programming techniques is
                 premature convergence, which emerges during evolution
                 as a progressive loss of variability among individuals
                 in the population. Moreover, the mechanisms according
                 to which individuals are created, recombined and
                 evaluated have of course strong influence on the
                 chances of success. Increasing variability of the
                 population and expressivity of the genotype are then
                 major issues for genetic programming techniques.

                 The aim of this paper is to investigate if a hybrid,
                 tree-based GP implementation written for symbolic
                 regression purposes can be improved in terms of
                 reliability and precision of the results both by
                 several modifications of the standard GP components and
                 by pre-processing the input data set.

                 In order to increase variability, the effect of a
                 simple archive updating strategy and of a periodical
                 killing of a large part of the population (with the
                 insertion of new and composed individuals) is assessed.
                 As a promising measure to preserve variation among
                 individuals, a MinMax approach in the definition of the
                 fitness function is also proposed and tested as an
                 alternative to the plain aggregating approach.

                 With regard to expressivity, a simple solution
                 consisting in the definition of a unary function that
                 introduces a translation in the argument of the
                 function itself is put forward.

                 Other experiments are performed to assess if the
                 redefinition of the fitness function using a normalised
                 error can have beneficial effects on the evolution, as
                 an alternative to the common root mean square

                 Finally, the splitting of the input data set in two
                 different subsets, respectively for parameter tuning
                 and fitness evaluation, is investigated.",
  notes =        "SQP Rosenbrock KILLandFILL Kotanchek RatPol2D Hock
                 Branin-Hoo Salustowicz session1_presentation5.pdf is

                 School of Civil Engineering, University of Leeds, LS2
                 9JT, UK",

Genetic Programming entries for Umberto Armani Vassili V Toropov Andrey Polynkin Osvaldo M Querin Luis F Alvarez