GEFPSO: A Framework for PSO Optimization based on Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Pericles Barbosa Miranda and 
                 Ricardo Bastos Prudencio",
  title =        "GEFPSO: A Framework for PSO Optimization based on
                 Grammatical Evolution",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1087--1094",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "",
  DOI =          "doi:10.1145/2739480.2754819",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this work, we propose a framework to automatically
                 generate effective PSO designs by adopting Grammatical
                 Evolution (GE). In the proposed framework, GE searches
                 for adequate structures and parameter values (e.g.,
                 acceleration constants, velocity equations and
                 different particles' topology) in order to evolve the
                 PSO design. For this, a high-level Backus--Naur Form
                 (BNF) grammar was developed, representing the search
                 space of possible PSO designs. In order to verify the
                 performance of the proposed method, we performed
                 experiments using 16 diverse continuous optimization
                 problems, with different levels of difficulty. In the
                 performed experiments, we identified the parameters and
                 components that most affected the PSO performance, as
                 well as identified designs that could be reused across
                 different problems. We also demonstrated that the
                 proposed method generates useful designs which achieved
                 competitive solutions when compared to well succeeded
                 algorithms from the literature.",
  notes =        "Also known as \cite{2754819} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Pericles Barbosa Miranda Ricardo Bastos Cavalcante Prudencio