Acceleration of Genetic Programming by Hierarchical Structure Learning: A Case Study on Image Recognition Program Synthesis

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Watchareeruetai:2009:IS,
  author =       "Ukrit Watchareeruetai and Tetsuya Matsumoto and 
                 Noboru Ohnishi and Hiroaki Kudo and Yoshinori Takeuchi",
  title =        "Acceleration of Genetic Programming by Hierarchical
                 Structure Learning: A Case Study on Image Recognition
                 Program Synthesis",
  journal =      "IEICE Transactions on Information and Systems",
  year =         "2009",
  volume =       "E92-D",
  number =       "10",
  pages =        "2094--2102",
  month =        oct,
  email =        "ukrit@ieee.org",
  publisher =    "IEICE",
  keywords =     "genetic algorithms, genetic programming, hierarchical
                 structure acceleration, learning node, training
                 subsets, population integration",
  ISSN =         "0916-8532",
  URL =          "http://search.ieice.org/bin/summary.php?id=e92-d_10_2094&category=D&year=2009&lang=E&abst=",
  abstract =     "We propose a learning strategy for acceleration in
                 learning speed of genetic programming (GP), named
                 hierarchical structure GP (HSGP). The HSGP exploits
                 multiple learning nodes (LNs) which are connected in a
                 hierarchical structure, e.g., a binary tree. Each LN
                 runs conventional evolutionary process to evolve its
                 own population, and sends the evolved population into
                 the connected higher-level LN. The lower-level LN
                 evolves the population with a smaller subset of
                 training data. The higher-level LN then integrates the
                 evolved population from the connected lower-level LNs
                 together, and evolves the integrated population further
                 by using a larger subset of training data. In HSGP,
                 evolutionary processes are sequentially executed from
                 the bottom-level LNs to the top-level LN which evolves
                 with the entire training data. In the experiments, we
                 adopt conventional GPs and the HSGPs to evolve image
                 recognition programs for given training images. The
                 results show that the use of hierarchical structure
                 learning can significantly improve learning speed of
                 GPs. To achieve the same performance, the HSGPs need
                 only 30-40percent of the computation cost needed by
                 conventional GPs.",
}

Genetic Programming entries for Ukrit WatchAreeruetai Tetsuya Matsumoto Noboru Ohnishi Hiroaki Kudo Yoshinori Takeuchi

Citations