Knowledge reuse in genetic programming applied to visual learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{1277318,
  author =       "Wojciech Jaskowski and Krzysztof Krawiec and 
                 Bartosz Wieloch",
  title =        "Knowledge reuse in genetic programming applied to
                 visual learning",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1790--1797",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1790.pdf",
  DOI =          "doi:10.1145/1276958.1277318",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming,
                 Genetics-Based Machine Learning, knowledge reuse,
                 pattern recognition",
  abstract =     "We propose a method of knowledge reuse for an ensemble
                 of genetic programming-based learners solving a visual
                 learning task. First, we introduce a visual learning
                 method that uses genetic programming individuals to
                 represent hypotheses. Individuals-hypotheses process
                 image representation composed of visual primitives
                 derived from the training images that contain objects
                 to be recognised. The process of recognition is
                 generative, i.e., an individual is supposed to restore
                 the shape of the processed object by drawing its
                 reproduction on a separate canvas. This canonical
                 method is extended with a knowledge reuse mechanism
                 that allows a learner to import genetic material from
                 hypotheses that evolved for the other decision classes
                 (object classes). We compare the performance of the
                 extended approach to the basic method on a real-world
                 tasks of handwritten character recognition, and
                 conclude that knowledge reuse leads to significant
                 convergence speedup and, more importantly,
                 significantly reduces the risk of overfitting.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Wojciech Jaskowski Krzysztof Krawiec Bartosz Wieloch

Citations