Functional modularity for genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Krzysztof Krawiec and Bartosz Wieloch",
  title =        "Functional modularity for genetic programming",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "995--1002",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1145/1569901.1570037",
  abstract =     "In this paper we introduce, formalize, and
                 experimentally validate a novel concept of functional
                 modularity for Genetic Programming (GP). We rely on
                 module definition that is most natural for GP: a piece
                 of program code (subtree). However, as opposed to
                 syntax-based approaches that abstract from the actual
                 computation performed by a module, we analyze also its
                 semantic using a set of fitness cases. In particular,
                 the central notion of this approach is subgoal, an
                 entity that embodies module's desired semantic and is
                 used to evaluate module candidates. As the cardinality
                 of the space of all subgoals is exponential with
                 respect to the number of fitness cases, we introduce
                 monotonicity to assess subgoals' potential utility for
                 searching for good modules. For a given subgoal and a
                 sample of modules, monotonicity measures the
                 correlation of subgoal's distance from module's
                 semantics and the fitness of the solution the module is
                 part of. In the experimental part we demonstrate how
                 these concepts may be used to describe and quantify the
                 modularity of two simple problems of Boolean function
                 synthesis. In particular, we conclude that monotonicity
                 usefully differentiates two problems with different
                 nature of modularity, allows us to tell apart the
                 useful subgoals from the other ones, and may be
                 potentially used for problem decomposition and enhance
                 the efficiency of evolutionary search.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",

Genetic Programming entries for Krzysztof Krawiec Bartosz Wieloch