Fitness Landscape based Parameter Estimation for Robust Taboo Search

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Andreas Beham and Erik Pitzer and 
                 Michael Affenzeller",
  title =        "Fitness Landscape based Parameter Estimation for
                 Robust Taboo Search",
  booktitle =    "Computer Aided Systems Theory, Eurocast 2013",
  year =         "2013",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "8111",
  series =       "LNCS",
  pages =        "292--299",
  address =      "Las Palmas, Spain",
  month =        "10-15 " # feb,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Problem
                 Instance, Problem Size, Fitness Landscape, Quadratic
                 Assignment Problem, Large Problem Size",
  isbn13 =       "978-3-642-53856-8",
  URL =          "",
  DOI =          "doi:10.1007/978-3-642-53856-8_37",
  abstract =     "Metaheuristic optimization algorithms are general
                 optimization strategies suited to solve a range of
                 real-world relevant optimization problems. Many
                 metaheuristics expose parameters that allow to tune the
                 effort that these algorithms are allowed to make and
                 also the strategy and search behaviour [1]. Adjusting
                 these parameters allows to increase the algorithms
                 performances with respect to different problem- and
                 problem instance characteristics.",

Genetic Programming entries for Andreas Beham Erik Pitzer Michael Affenzeller