Designing and adapting existing metaheuristic algorithms for problems require careful tuning of the algorithm's parameters as their performance is often highly-dependent on these parameters. Most existing parameter tuning tools are agnostic to the problem features (e.g., number of resources and tasks, resource capacities, etc.).

We are interested in developing tools able to map the algorithm's parameters to the problem features and generate feature-dependent parameters to improve the efficiency of the used algorithms.