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

Maxim Buzdalov

Maxim Buzdalov

Aberystwyth University