Efficient Construction of Image Feature Extraction Programs by Using Linear Genetic Programming with Fitness Retrieval and Intermediate-Result Caching

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

@InCollection{series/sci/WatchareeruetaiMTKO09,
  author =       "Ukrit Watchareeruetai and Tetsuya Matsumoto and 
                 Yoshinori Takeuchi and Hiroaki Kudo and 
                 Noboru Ohnishi",
  title =        "Efficient Construction of Image Feature Extraction
                 Programs by Using Linear Genetic Programming with
                 Fitness Retrieval and Intermediate-Result Caching",
  booktitle =    "Foundations of Computational Intelligence - Volume 4:
                 Bio-Inspired Data Mining",
  publisher =    "Springer",
  year =         "2009",
  volume =       "204",
  editor =       "Ajith Abraham and Aboul Ella Hassanien and 
                 Andr{\'e} Carlos Ponce Leon Ferreira {de Carvalho}",
  series =       "Studies in Computational Intelligence",
  pages =        "355--375",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-01087-3",
  URL =          "http://dx.doi.org/10.1007/978-3-642-01088-0",
  DOI =          "doi:10.1007/978-3-642-01088-0_15",
  abstract =     "This chapter describes a bio-inspired approach for
                 automatic construction of feature extraction programs
                 (FEPs) for a given object recognition problem. The goal
                 of the automatic construction of FEPs is to cope with
                 the difficulties in FEP design. Linear genetic
                 programming (LGP) [4], a variation of evolutionary
                 algorithms, is adopted. A population of FEPs is
                 constructed from a set of basic image processing
                 operations-which are used as primitive operators (POs),
                 and their performances are optimised in the
                 evolutionary process. Here we describe two techniques
                 that improve the efficiency of the LGP-based program
                 construction. One is to use fitness retrieval to avoid
                 wasteful evaluations of the programs discovered before.
                 The other one is to use intermediate-result caching, to
                 avoid evaluation of the program-parts which were
                 recently executed. The experimental results show that
                 much computation time of the LGP-based FEP construction
                 can be reduced by using these two techniques.",
  bibdate =      "2010-04-20",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/series/sci/sci204.html#WatchareeruetaiMTKO09",
}

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

Citations