Genetic programming for cross-task knowledge sharing

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

@InProceedings{1277281,
  author =       "Wojciech Jaskowski and Krzysztof Krawiec and 
                 Bartosz Wieloch",
  title =        "Genetic programming for cross-task knowledge sharing",
  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 =        "1620--1627",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1620.pdf",
  DOI =          "doi:10.1145/1276958.1277281",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, knowledge
                 sharing, multitask learning, representation",
  abstract =     "We consider multi-task learning of visual concepts
                 within genetic programming (GP) framework. The proposed
                 method evolves a population of GP individuals, with
                 each of them composed of several GP trees that process
                 visual primitives derived from input images. The two
                 main trees are delegated to solving two different
                 visual tasks and are allowed to share knowledge with
                 each other by calling the remaining GP trees
                 (sub-functions) included in the same individual. The
                 method is applied to the visual learning task of
                 recognising simple shapes, using generative approach
                 based on visual primitives, introduced in [17]. We
                 compare this approach to a reference method devoid of
                 knowledge sharing, and conclude that in the worst case
                 cross-task learning performs equally well, and in many
                 cases it leads to significant performance improvements
                 in one or both solved tasks.",
  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