Regularization Approach to Inductive Genetic Programming

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@Article{nikolaev:2001:TEC,
  author =       "Nikolay Y. Nikolaev and Hitoshi Iba",
  title =        "Regularization Approach to Inductive Genetic
                 Programming",
  journal =      "IEEE Transactions on Evolutionary Computing",
  year =         "2001",
  volume =       "54",
  number =       "4",
  pages =        "359--375",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, learning
                 (artificial intelligence), tree data structures, tree
                 searching, data mining, Kolmogorov-Gabor polynomials,
                 inductive genetic programming, learning polynomials,
                 multivariate polynomials, tree structures, statistical
                 bias, tree nodes, data mining, regularization, time
                 series prediction, STROGANOFF,local search",
  URL =          "http://ieeexplore.ieee.org/iel5/4235/20398/00942530.pdf?isNumber=20398",
  DOI =          "doi:10.1109/4235.942530",
  size =         "17 pages",
  abstract =     "This paper presents an approach to regularization of
                 inductive genetic programming tuned for learning
                 polynomials. The objective is to achieve optimal
                 evolutionary performance when searching high-order
                 multivariate polynomials represented as tree
                 structures. We show how to improve the genetic
                 programming of polynomials by balancing its statistical
                 bias with its variance. Bias reduction is achieved by
                 employing a set of basis polynomials in the tree nodes
                 for better agreement with the examples. Since this
                 often leads to over-fitting, such tendencies are
                 counteracted by decreasing the variance through
                 regularization of the fitness function. We demonstrate
                 that this balance facilitates the search as well as
                 enables discovery of parsimonious, accurate, and
                 predictive polynomials. The experimental results given
                 show that this regularization approach outperforms
                 traditional genetic programming on benchmark data
                 mining and practical time-series prediction tasks.",
  notes =        "Recombinative Guidance, FDC, MDL, IGP, Mackey-Glass,
                 ANN",
}

Genetic Programming entries for Nikolay Nikolaev Hitoshi Iba

Citations