Discovering a domain alphabet

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Michael D. Schmidt and Hod Lipson",
  title =        "Discovering a domain alphabet",
  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 =        "1083--1090",
  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:10.1145/1569901.1570047",
  abstract =     "A key to the success of any genetic programming
                 process is the use of a good alphabet of atomic
                 building blocks from which solutions can be evolved
                 efficiently. An alphabet that is too granular may
                 generate an unnecessarily large search space; an
                 inappropriately coarse grained alphabet may bias or
                 prevent finding optimal solutions. Here we introduce a
                 method that automatically identifies a small alphabet
                 for a problem domain. We process solutions on the
                 complexity-optimality Pareto front of a number of
                 sample systems and identify terms that appear
                 significantly more frequently than merited by their
                 size. These terms are then used as basic building
                 blocks to solve new problems in the same problem
                 domain. We demonstrate this process on symbolic
                 regression for a variety of physics problems. The
                 method discovers key terms relating to concepts such as
                 energy and momentum. A significant performance
                 enhancement is demonstrated when these terms are then
                 used as basic building blocks on new physics problems.
                 We suggest that identifying a problem-specific alphabet
                 is key to scaling evolutionary methods to higher
                 complexity systems.",
  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 Michael D Schmidt Hod Lipson