Research: Architecture and Design of the HeuristicLab Optimization Environment.bib

File Architecture and Design of the HeuristicLab Optimization Environment.bib, 2.4 KB (added by swagner, 3 years ago)
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1@INBOOK{wagner2014,
2  chapter = {Architecture and Design of the HeuristicLab Optimization Environment},
3  pages = {197--261},
4  title = {Advanced Methods and Applications in Computational Intelligence},
5  publisher = {Springer},
6  year = {2014},
7  editor = {Klempous, Ryszard and Nikodem, Jan and Jacak, Witold and Chaczko,
8  Zenon},
9  author = {Wagner, Stefan and Kronberger, Gabriel and Beham, Andreas and Kommenda,
10  Michael and Scheibenpflug, Andreas and Pitzer, Erik and Vonolfen,
11  Stefan and Kofler, Monika and Winkler, Stephan and Dorfer, Viktoria
12  and Affenzeller, Michael},
13  volume = {6},
14  series = {Topics in Intelligent Engineering and Informatics},
15  abstract = {Many optimization problems cannot be solved by classical mathematical
16  optimization techniques due to their complexity and the size of the
17  solution space. In order to achieve solutions of high quality though,
18  heuristic optimization algorithms are frequently used. These algorithms
19  do not claim to find global optimal solutions, but offer a reasonable
20  tradeoff between runtime and solution quality and are therefore especially
21  suitable for practical applications. In the last decades the success
22  of heuristic optimization techniques in many different problem domains
23  encouraged the development of a broad variety of optimization paradigms
24  which often use natural processes as a source of inspiration (as
25  for example evolutionary algorithms, simulated annealing, or ant
26  colony optimization). For the development and application of heuristic
27  optimization algorithms in science and industry, mature, flexible
28  and usable software systems are required. These systems have to support
29  scientists in the development of new algorithms and should also enable
30  users to apply different optimization methods on specific problems
31  easily. The architecture and design of such heuristic optimization
32  software systems impose many challenges on developers due to the
33  diversity of algorithms and problems as well as the heterogeneous
34  requirements of the different user groups. In this chapter the authors
35  describe the architecture and design of their optimization environment
36  HeuristicLab which aims to provide a comprehensive system for algorithm
37  development, testing, analysis and generally the application of heuristic
38  optimization methods on complex problems.},
39  url = {http://link.springer.com/chapter/10.1007/978-3-319-01436-4_10}
40}