HeuristicLab: A Generic and Extensible Optimization Environment

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


Today numerous variants of heuristic optimization algorithms are used to solve different kinds of optimization problems. This huge variety makes it very difficult to reuse already implemented algorithms or problems. In this paper the authors describe a generic, extensible, and paradigm-independent optimization environment that strongly abstracts the process of heuristic optimization. By providing a well organized and strictly separated class structure and by introducing a generic operator concept for the interaction between algorithms and problems, HeuristicLab makes it possible to reuse an algorithm implementation for the attacking of lots of different kinds of problems and vice versa. Consequently HeuristicLab is very well suited for rapid prototyping of new algorithms and is also useful for educational support due to its state-of-the-art user interface, its self-explanatory API and the use of modern programming concepts.
Original languageEnglish
Title of host publicationAdaptive and Natural Computing Algorithms
PublisherSpringer Vieweg
ISBN (Print)3-211-24934-6
Publication statusPublished - 2005
Event7th International Conference on Adaptive and Natural Computing Algorithms - Coimbra, Portugal
Duration: 21 Mar 200523 Mar 2005


Conference7th International Conference on Adaptive and Natural Computing Algorithms
Internet address


  • Heuristic Optimization
  • Plugins
  • Software Architecture

Fingerprint Dive into the research topics of 'HeuristicLab: A Generic and Extensible Optimization Environment'. Together they form a unique fingerprint.

Cite this