Improving GP generalization: a variance-based layered learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{AmirHaeri:2014:GPEM,
  author =       "Maryam {Amir Haeri} and Mohammad Mehdi Ebadzadeh and 
                 Gianluigi Folino",
  title =        "Improving GP generalization: a variance-based layered
                 learning approach",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "1",
  pages =        "27--55",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, VBLL-GP,
                 Generalisation, Layered learning, Over fitting,
                 Variance",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-014-9220-6",
  size =         "29 pages",
  abstract =     "This paper introduces a new method that improves the
                 generalisation ability of genetic programming (GP) for
                 symbolic regression problems, named variance-based
                 layered learning GP. In this approach, several
                 datasets, called primitive training sets, are derived
                 from the original training data. They are generated
                 from less complex to more complex, for a suitable
                 complexity measure. The last primitive dataset is still
                 less complex than the original training set. The
                 approach decomposes the evolution process into several
                 hierarchical layers. The first layer of the evolution
                 starts using the least complex (smoothest) primitive
                 training set. In the next layers, more complex
                 primitive sets are given to the GP engine. Finally, the
                 original training data is given to the algorithm. We
                 use the variance of the output values of a function as
                 a measure of the functional complexity. This measure is
                 used in order to generate smoother training data, and
                 controlling the functional complexity of the solutions
                 to reduce the overfitting. The experiments, conducted
                 on four real-world and three artificial symbolic
                 regression problems, demonstrate that the approach
                 enhances the generalization ability of the GP, and
                 reduces the complexity of the obtained solutions.",
  notes =        "LD50, Bioavailablity, concrete, Pollen, UBall5D,
                 RatPol2D

                 Author Affiliations: 1. Department of Computer
                 Engineering and Information Technology, Amirkabir
                 University of Technology, Tehran, Iran 2. ICAR-CNR,
                 Rende, Italy",
}

Genetic Programming entries for Maryam Amir Haeri Mohammad Mehdi Ebadzadeh Gianluigi Folino

Citations