Architecture and Design of the HeuristicLab Optimization Environment

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Wagner2014,
  author =       "S. Wagner and G. Kronberger and A. Beham and 
                 M. Kommenda and A. Scheibenpflug and E. Pitzer and 
                 S. Vonolfen and M. Kofler and S. Winkler and V. Dorfer and 
                 M. Affenzeller",
  title =        "Architecture and Design of the {HeuristicLab}
                 Optimization Environment",
  booktitle =    "First Australian Conference on the Applications of
                 Systems Engineering, ACASE",
  year =         "2012",
  editor =       "Robin Braun and Zenon Chaczko and Franz Pichler",
  volume =       "6",
  series =       "Topics in Intelligent Engineering and Informatics",
  pages =        "197--261",
  address =      "Sydney, Australia",
  month =        feb # " 6-8",
  publisher =    "Springer International Publishing",
  note =         "Selected and updated papers",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-01435-7",
  URL =          "http://dx.doi.org/10.1007/978-3-319-01436-4_10",
  DOI =          "doi:10.1007/978-3-319-01436-4_10",
  abstract =     "Many optimisation problems cannot be solved by
                 classical mathematical optimization techniques due to
                 their complexity and the size of the solution space. In
                 order to achieve solutions of high quality though,
                 heuristic optimization algorithms are frequently used.
                 These algorithms do not claim to find global optimal
                 solutions, but offer a reasonable tradeoff between
                 runtime and solution quality and are therefore
                 especially suitable for practical applications. In the
                 last decades the success of heuristic optimization
                 techniques in many different problem domains encouraged
                 the development of a broad variety of optimization
                 paradigms which often use natural processes as a source
                 of inspiration (as for example evolutionary algorithms,
                 simulated annealing, or ant colony optimization). For
                 the development and application of heuristic
                 optimization algorithms in science and industry,
                 mature, flexible and usable software systems are
                 required. These systems have to support scientists in
                 the development of new algorithms and should also
                 enable users to apply different optimization methods on
                 specific problems easily. The architecture and design
                 of such heuristic optimization software systems impose
                 many challenges on developers due to the diversity of
                 algorithms and problems as well as the heterogeneous
                 requirements of the different user groups. In this
                 chapter the authors describe the architecture and
                 design of their optimization environment HeuristicLab
                 which aims to provide a comprehensive system for
                 algorithm development, testing, analysis and generally
                 the application of heuristic optimization methods on
                 complex problems.",
  notes =        "Published by Springer 2014. as Advanced Methods and
                 Applications in Computational Intelligence. Series
                 editors Klempous, Ryszard and Nikodem, Jan and Jacak,
                 Witold and Chaczko, Zenon",
}

Genetic Programming entries for Stefan Wagner Gabriel Kronberger Andreas Beham Michael Kommenda Andreas Scheibenpflug Erik Pitzer Stefan Vonolfen Monika Kofler Stephan M Winkler Viktoria Dorfer Michael Affenzeller

Citations