Download call for papers.

Contributions to the workshop should address the combination and comparison of different metaheuristic components and concepts. Also negative results (e.g., a component shows poor performance for the majority of test instances) are of considerable importance in hybridization. Such results have often been ignored in standard metaheuristics research. Furthermore, the enlarged number of parameters of the combined concepts attracts more attention to the design of algorithms and the tuning of parameters.

This workshop aims at papers that give good examples for carefully designed and well-analyzed hybrid metaheuristics. Researchers are explicitly encouraged to address statistical validity of their results. The extraction of guidelines for the general design of hybrid metaheuristics would be desirable.

The scope of this workshop includes, but is not limited to:
  • novel combinations of components from different metaheuristics
  • hybridization of metaheuristics and AI/OR techniques
  • low-level hybridization
  • high-level hybridization
  • portfolio techniques
  • expert systems
  • co-operative search
  • taxonomy
  • terminology
  • classification of hybrid metaheuristics
  • co-evolution techniques
  • automated parameter tuning
  • empirical and statistical comparison
  • theoretic aspects of hybridization
  • parallelization
  • software libraries