| 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 | } |
|---|
