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HeuristicLab: A Generic and Extensible Optimization Environment

  • S. Wagner
  • M. Affenzeller

Abstract

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.

Keywords

Genetic Algorithm Graphical User Interface Rapid Prototype Travel Salesman Problem Travel Salesman Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • S. Wagner
    • 1
  • M. Affenzeller
    • 1
  1. 1.Institute for Formal Models and VerificationJohannes Kepler UniversityLinzAustria

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