Parameter tuning of evolutionary reactions systems

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

  author =       "Mauro Castelli and Luca Manzoni and 
                 Leonardo Vanneschi",
  title =        "Parameter tuning of evolutionary reactions systems",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "727--734",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330265",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Reaction systems is a formalism inspired by chemical
                 reactions introduced by Rozenberg and Ehrenfeucht.
                 Recently, an evolutionary algorithm based on this
                 formalism, called Evolutionary Reaction Systems, has
                 been presented. This new algorithm proved to have
                 comparable performances to other well-established
                 machine learning methods, like genetic programming,
                 neural networks and support vector machines on both
                 artificial and real-life problems. Even if the results
                 are encouraging, to make Evolutionary Reaction Systems
                 an established evolutionary algorithm, an in depth
                 analysis of the effect of its parameters on the search
                 process is needed, with particular focus on those
                 parameters that are typical of Evolutionary Reaction
                 Systems and do not have a counterpart in traditional
                 evolutionary algorithms. Here we address this problem
                 for the first time. The results we present show that
                 one particular parameter, between the ones tested, has
                 a great influence on the performances of Evolutionary
                 Reaction Systems, and thus its setting deserves
                 practitioners' particular attention: the number of
                 symbols used to represent the reactions that compose
                 the system. Furthermore, this work represents a first
                 step towards the definition of a set of default
                 parameter values for Evolutionary Reaction Systems,
                 that should facilitate their use for beginners or
                 inexpert practitioners.",
  notes =        "Also known as \cite{2330265} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for Mauro Castelli Luca Manzoni Leonardo Vanneschi