Determination of optimum genetic parameters for symbolic non-linear regression-like problems in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Chaudhary:2009:INMIC,
  author =       "U. K. Chaudhary and M. Iqbal",
  title =        "Determination of optimum genetic parameters for
                 symbolic non-linear regression-like problems in genetic
                 programming",
  booktitle =    "IEEE 13th International Multitopic Conference, INMIC
                 2009",
  year =         "2009",
  month =        dec,
  pages =        "1--5",
  keywords =     "genetic algorithms, genetic programming, Matlab,
                 elitism, halfelitism-roulette, keepbest-doubletour,
                 optimum genetic parameters, replace-doubletour,
                 replace-lexictour, replace-tournament, symbolic
                 non-linear regression-like problems, mathematics
                 computing, regression analysis",
  DOI =          "doi:10.1109/INMIC.2009.5383162",
  abstract =     "Parametric studies have been carried out for the
                 quartic-polynomial regression problem demonstrated in
                 the Genetic Programming (GP) v3 toolbox of Matlab. Many
                 classification schemes and modeling issues are
                 polynomial based. Every possible combination
                 originating from all available options between the two
                 genetic parameters namely 'elitism' and 'sampling' has
                 been analyzed while keeping all other parameters as
                 fixed. Three performance parameters namely, execution
                 time of a given GP run, quickness of convergence to
                 reach the required fitness and the most important,
                 fitness improvement factor per generation have been
                 studied. In terms of the last mentioned performance
                 parameter, being an indicative of diversity, it is
                 shown that the best particular combination is
                 'halfelitism-sus' if naming in the general format of
                 'elitism-sampling' is used. On the average, this
                 combination went on improving the fitness value (of the
                 best so far individual) in more than 78percent of
                 generations as the GP simulations progressed towards
                 the required solution. Secondly, halfelitism-roulette
                 took, on the average, as less as 6.8 generations to
                 complete a GP run outperforming other combinations in
                 terms of quickness of convergence with again,
                 halfelitism-sus as second best consuming 7.4
                 generations to reach at the desired quartic genre. In
                 spite of its promising average values, this combination
                 showed a contrasting behavior depending upon the
                 auto-evolution process at the start of a given GP run.
                 Either it took on a right track and converged to the
                 solution efficiently or it de-tracked in the very
                 beginning and lost its performance regarding the three
                 aforementioned parameters. Furthermore, it was found
                 that for the combinations replace-doubletour and
                 keepbest-doubletour giving the best two execution times
                 (in seconds) to complete a given GP run, their results
                 were least encouraging regarding the other performance
                 parameters. Also, in contrast to some combinations such
                 as, replace-tournament and replace-lexictour, other
                 combinations worked satisfactorily well in at least one
                 of the three performances studied.",
  notes =        "Also known as \cite{5383162}",
}

Genetic Programming entries for U K Chaudhary M Iqbal

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