HeuristicLab grid – a flexible and extensible environment for parallel heuristic optimization. Heuristic optimization techniques turned out to be very well suited for attacking various kinds of problems. However, when it comes to practical applications like scheduling problems, route planning, etc., also these algorithms still suffer from a very long running time mainly due to the rather large problem instances relevant in real world applications. Consequently, parallel optimization methods like parallel genetic algorithms are widely used to overcome this handicap. In this paper, the authors present a new environment for parallel heuristic optimization based upon the already proposed HeuristicLab. In contrast to other existing grid computing or parallel optimization projects, HeuristicLab grid offers the possibility of rapid and easy use of existing optimization algorithms and problems in a parallel way without the need of complex installation and maintenance. (Source: http://en.wikipedia.org/wiki/HeuristicLab)

References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Karimi-Mamaghan, Maryam; Mohammadi, Mehrdad; Meyer, Patrick; Karimi-Mamaghan, Amir Mohammad; Talbi, El-Ghazali: Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art (2022)
  2. Vonolfen, Stefan; Affenzeller, Michael: Distribution of waiting time for dynamic pickup and delivery problems (2016)
  3. Xie, Xiao-Feng; Liu, Jiming; Wang, Zun-Jing: A cooperative group optimization system (2014) ioport
  4. Alba, Enrique; Luque, Gabriel; Nesmachnow, Sergio: Parallel metaheuristics: recent advances and new trends (2013)
  5. García-Sánchez, P.; González, J.; Castillo, P. A.; Arenas, M. G.; Merelo-Guervós, J. J.: Service oriented evolutionary algorithms (2013) ioport
  6. Vonolfen, Stefan; Affenzeller, Michael; Beham, Andreas; Lengauer, Efrem; Wagner, Stefan: Simulation-based evolution of resupply and routing policies in rich vendor-managed inventory scenarios (2013)
  7. Pérez-Ortega, J.; Pazos R., R. A.; Ruiz-Vanoye, J. A.; Frausto-Solís, J.; González-Barbosa, J. J.; Fraire-Huacuja, H. J.; Díaz-Parra, O.: A genetic distance metric to discriminate the selection of algorithms for the general ATSP problem (2010)
  8. Affenzeller, Michael; Winkler, Stephan; Wagner, Stefan; Beham, Andreas: Genetic algorithms and genetic programming. Modern concepts and practical applications. (2009)
  9. Winkler, S. M.; Affenzeller, M.; Wagner, S.: On the reliability of nonlinear modeling using enhanced genetic programming techniques (2009)
  10. Winkler, Stephan M.; Affenzeller, Michael; Wagner, Stefan: Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis (2009) ioport
  11. Winkler, Stephan; Affenzeller, Michael; Wagner, Stefan: Advanced genetic programming based machine learning (2007)
  12. Wagner, Stefan; Affenzeller, Michael: HeuristicLab grid -- a flexible and extensible environment for parallel heuristic optimization (2004)

Further publications can be found at: http://heal.heuristiclab.com/publications