Geometric Differential Evolution for Combinatorial and Programs Spaces

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

  author =       "A. Moraglio and J. Togelius and S. Silva",
  title =        "Geometric Differential Evolution for Combinatorial and
                 Programs Spaces",
  journal =      "Evolutionary Computation",
  year =         "2013",
  volume =       "21",
  number =       "4",
  pages =        "591--624",
  month =        "Winter",
  keywords =     "genetic algorithms, genetic programming, DE, NK, TSP,
                 Sudoku, Differential evolution, representations,
                 principled design of search operators, combinatorial
                 spaces, theory",
  ISSN =         "1063-6560",
  URL =          "",
  DOI =          "doi:10.1162/EVCO_a_00099",
  size =         "34 pages",
  abstract =     "Geometric differential evolution (GDE) is a recently
                 introduced formal generalisation of traditional
                 differential evolution (DE) that can be used to derive
                 specific differential evolution algorithms for both
                 continuous and combinatorial spaces retaining the same
                 geometric interpretation of the dynamics of the DE
                 search across representations. In this article, we
                 first review the theory behind the GDE algorithm, then,
                 we use this framework to formally derive specific GDE
                 for search spaces associated with binary strings,
                 permutations, vectors of permutations and genetic
                 programs. The resulting algorithms are
                 representation-specific differential evolution
                 algorithms searching the target spaces by acting
                 directly on their underlying representations. We
                 present experimental results for each of the new
                 algorithms on a number of well-known problems
                 comprising NK-landscapes, TSP, and Sudoku, for binary
                 strings, permutations and vectors of permutations. We
                 also present results for the Regression, Artificial
                 Ant, Parity and Multiplexer problems within the genetic
                 programming domain. Experiments show that overall the
                 new DE algorithms are competitive with well-tuned
                 standard search algorithms.",
  notes =        "Posted online on 27 Dec 2012.
                 oai:CiteSeerX.psu: is draft?",

Genetic Programming entries for Alberto Moraglio Julian Togelius Sara Silva