An Evolutionary Approach to Complex System Regulation Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{amarteifio:2004:AL,
  author =       "Saoirse Amarteifio and Michael O'Neill",
  title =        "An Evolutionary Approach to Complex System Regulation
                 Using Grammatical Evolution",
  booktitle =    "Artificial Life {XI} Ninth International Conference on
                 the Simulation and Synthesis of Living Systems",
  year =         "2004",
  editor =       "Jordan Pollack and Mark Bedau and Phil Husbands and 
                 Takashi Ikegami and Richard A. Watson",
  pages =        "551--556",
  address =      "Boston, Massachusetts",
  month =        "12-15 " # sep,
  publisher =    "The MIT Press",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  ISBN =         "0-262-66183-7",
  URL =          "http://ncra.ucd.ie/papers/alife2004.pdf",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6278781",
  size =         "6 pages",
  abstract =     "Motivated by difficulties in engineering adaptive
                 distributed systems, we consider a method to evolve
                 cooperation in swarms to model dynamical systems. We
                 consider an information processing swarm model that we
                 find to be useful in studying control methods for
                 adaptive distributed systems and attempt to evolve
                 systems that form consistent patterns through the
                 interaction of constituent agents or particles. This
                 model considers artificial ants as walking sensors in
                 an information-rich environment. Grammatical Evolution
                 is combined with this swarming model as we evolve an
                 ant's response to information. The fitness of the swarm
                 depends on information processing by individual ants,
                 which should lead to appropriate macroscopic spatial
                 and/or temporal patterns. We discuss three primary
                 issues, which are tractability, representation and
                 fitness evaluation of dynamical systems and show how
                 Grammatical Evolution supports a promising approach to
                 addressing these concerns",
  notes =        "ALIFE9",
}

Genetic Programming entries for Saoirse Amarteifio Michael O'Neill

Citations