Improving GP classification performance by injection of decision trees

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Rikard Konig and Ulf Johansson and Tuve Lofstrom and 
                 Lars Niklasson",
  title =        "Improving GP classification performance by injection
                 of decision trees",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "This paper presents a novel hybrid method combining
                 genetic programming and decision tree learning. The
                 method starts by estimating a benchmark level of
                 reasonable accuracy, based on decision tree performance
                 on bootstrap samples of the training set. Next, a
                 normal GP evolution is started with the aim of
                 producing an accurate GP. At even intervals, the best
                 GP in the population is evaluated against the accuracy
                 benchmark. If the GP has higher accuracy than the
                 benchmark, the evolution continues normally until the
                 maximum number of generations is reached. If the
                 accuracy is lower than the benchmark, two things
                 happen. First, the fitness function is modified to
                 allow larger GPs, able to represent more complex
                 models. Secondly, a decision tree with increased size
                 and trained on a bootstrap of the training data is
                 injected into the population. The experiments show that
                 the hybrid solution of injecting decision trees into a
                 GP population gives synergetic effects producing
                 results that are better than using either technique
                 separately. The results, from 18 UCI data sets, show
                 that the proposed method clearly outperforms normal GP,
                 and is significantly better than the standard decision
                 tree algorithm.",
  DOI =          "doi:10.1109/CEC.2010.5585988",
  notes =        "WCCI 2010. Also known as \cite{5585988}",

Genetic Programming entries for Rikard Konig Ulf Johansson Tuve Lofstrom Lars Niklasson