DEAP - Enabling Nimbler Evolutions

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{de-rainville_2012_sigevolution,
  author =       "Francois-Michel {De Rainville} and 
                 Felix-Antoine Fortin and Marc-Andre Gardner and Marc Parizeau and 
                 Christian Gagne",
  title =        "DEAP - Enabling Nimbler Evolutions",
  journal =      "SIGEvolution newsletter of the ACM Special Interest
                 Group on Genetic and Evolutionary Computation",
  year =         "2012",
  volume =       "6",
  number =       "2",
  pages =        "17--26",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1931-8499",
  URL =          "http://www.sigevolution.org/issues/pdf/SIGEVOlution0602.pdf",
  URL =          "https://github.com/DEAP/notebooks",
  size =         "10 pages",
  abstract =     "DEAP is a Distributed Evolutionary Algorithm (EA)
                 framework written in Python and designed to help
                 researchers developing custom evolutionary algorithms.
                 Its design philosophy promotes explicit algorithms and
                 transparent data structures, in contrast with most
                 other evolutionary computation softwares that tend to
                 encapsulate standardised algorithms using the black-box
                 approach. This philosophy sets it apart as a rapid
                 prototyping framework for testing of new ideas in EA
                 research. An executable notebook version of this paper
                 is available at https://github.com/DEAP/notebooks.",
  notes =        "18 Feb 2014

                 . Building blocks for testing ideas . Rapid prototyping
                 . Fully transparent . Parallel ready . Exhaustively
                 documented . Available at
                 http://deap.gel.ulaval.ca

                 Distributed Island model. Genealogy tree

                 The presented examples covered only a small part of
                 DEAP's capabilities that include evolution strategies
                 (including CMA-ES), multi-objective optimisation
                 (NSGA-II and SPEA-II), co-evolution, particle swarm
                 optimisation PSO, as well as many benchmarks
                 (continuous, binary, regression, and moving peaks), and
                 examples (more than 40).

                 Departement de genie electrique et de genie
                 informatique - Universite Laval - Quebec (Quebec),
                 Canada",
}

Genetic Programming entries for Francois-Michel De Rainville Felix-Antoine Fortin Marc-Andre Gardner Marc Parizeau Christian Gagne

Citations