Memetic Self-Configuring Genetic Programming for Solving Machine Learning Problems

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

  author =       "Maria Semenkina and Eugene Semenkin",
  booktitle =    "4th IIAI International Congress on Advanced Applied
                 Informatics (IIAI-AAI)",
  title =        "Memetic Self-Configuring Genetic Programming for
                 Solving Machine Learning Problems",
  year =         "2015",
  pages =        "599--604",
  abstract =     "A hybridization of self-configuring genetic
                 programming algorithms (SelfCGPs) with a local search
                 in the space of trees is fulfilled to improve their
                 performance for symbolic regression problem solving and
                 artificial neural network automated design. The local
                 search is implemented with two neighbourhood systems
                 (1-level and 2-level neighbourhoods), three strategies
                 of a tree scanning ('full', 'incomplete' and
                 'truncated') and two ways of a movement between
                 adjacent trees (transition by the first improvement and
                 the steepest descent). The Lamarckian local search is
                 applied on each generation to ten percent of best
                 individuals. The performance of all developed memetic
                 algorithms is estimated on a representative set of test
                 problems of the functions approximation as well as on
                 real-world machine learning problems. It is shown that
                 developed memetic algorithms require comparable amount
                 of computational efforts but outperform the original
                 SelfCGPs both for the symbolic regression and neural
                 network design. The best variant of the local search
                 always uses the steepest descent but different tree
                 scanning strategies, namely, full scanning for the
                 solving of symbolic regression problems and incomplete
                 scanning for the neural network automated design.
                 Additional advantage of the approach proposed is a
                 possibility of the automated features selection.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IIAI-AAI.2015.290",
  month =        jul,
  notes =        "Also known as \cite{7373977}",

Genetic Programming entries for Maria Semenkina Eugene Semenkin