Geometric nelder-mead algorithm on the space of genetic programs

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Alberto Moraglio and Sara Silva",
  title =        "Geometric nelder-mead algorithm on the space of
                 genetic programs",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1307--1314",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001753",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The Nelder-Mead Algorithm (NMA) is a close relative of
                 Particle Swarm Optimization (PSO) and Differential
                 Evolution (DE). In recent work, PSO, DE and NMA have
                 been generalized using a formal geometric framework
                 that treats solution representations in a uniform way.
                 These formal algorithms can be used as templates to
                 derive rigorously specific PSO, DE and NMA for both
                 continuous and combinatorial spaces retaining the same
                 geometric interpretation of the search dynamics of the
                 original algorithms across representations. In previous
                 work, a geometric NMA has been derived for the binary
                 string representation and permutation representation.
                 Furthermore, PSO and DE have already been derived for
                 the space of genetic programs. In this paper, we
                 continue this line of research and derive formally a
                 specific NMA for the space of genetic programs. The
                 result is a Nelder-Mead Algorithm searching the space
                 of genetic programs by acting directly on their tree
                 representation. We present initial experimental results
                 for the new algorithm. The challenge tackled in the
                 present work compared with earlier work is that the
                 pair NMA and genetic programs is the most complex
                 considered so far. This combination raises a number of
                 issues and casts light on how algorithmic features can
                 interact with representation features to give rise to a
                 highly peculiar search behaviour.",
  notes =        "Also known as \cite{2001753} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",

Genetic Programming entries for Alberto Moraglio Sara Silva