We invite abstracts and presentations, either theory or practice-oriented (preferably on real world applications) that discuss any of (but not limited to) the issues of:

  • Metaheuristics including population-based methods (genetic algorithms, multi-objective evolutionary algorithms, memetic algorithms, genetic programming, particle swarm optimisation, etc.), trajectory methods (tabu search, simulated annealing, variable neighborhood search, etc.), and more.
  • Systems to build systems, particularly (meta/hyper-)heuristics or any other related (adaptive, self-tuning, reactive, self-improving, etc.) methods.
  • Interplay between data science (machine learning, statistics, etc.) and metaheuristics.
  • Developing the analytical /theoretical understanding of (meta/hyper-)heuristics.

We are particularly interested in:

  • Methods that are based on or improved/integrated with data science techniques and/or allow a more general use of metaheuristics and heuristics. For example, hyper-heuristics or adaptive search control systems.
  • Analyses that lead to some insight into the behaviour of one or more metaheuristics. For example, but not limited to, landscape analysis, stochastic analysis methods or modeling.



Ender Ozcan