Architecture and Design of the HeuristicLab Optimization Environment
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Wagner2014,
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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",
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title = "Architecture and Design of the {HeuristicLab}
Optimization Environment",
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booktitle = "First Australian Conference on the Applications of
Systems Engineering, ACASE",
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year = "2012",
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editor = "Robin Braun and Zenon Chaczko and Franz Pichler",
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volume = "6",
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series = "Topics in Intelligent Engineering and Informatics",
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pages = "197--261",
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address = "Sydney, Australia",
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month = feb # " 6-8",
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publisher = "Springer International Publishing",
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note = "Selected and updated papers",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-01435-7",
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URL = "http://dx.doi.org/10.1007/978-3-319-01436-4_10",
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DOI = "doi:10.1007/978-3-319-01436-4_10",
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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.",
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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