## 1 Introduction  
The scheduling problem is one of the oldest combinatorial optimization problems. Over the years, different approaches to its resolution have been developed. However, many of those approaches are impractical in real production environments, due to the fact of being inherently dynamic, with complex constraints and unexpected disruptions. In most real-world environments, scheduling is a progressive reactive process where the presence of real-time information continuously requires the reconsideration and review of predetermined plans. For combinatorial optimization problems such as scheduling, there is the need to select, from a discrete and finite set of data, the best subset satisfying certain criteria of economic and operational nature. The great challenge of these problems is to produce the closest possible solutions to the optimal solution, in a competitive time. Scheduling problems are combinatorial optimization problems classified as NP-hard, subject to constraints with a dynamic and very complex resolution nature. Among the several approaches for solving combinatorial optimization problems, there are the approximation methods in which the goal is to find near-optimal solutions in acceptable runtimes. Metaheuristics are included in this category, and many of them are inspired by nature. Metaheuristics are increasingly used in areas where complexity and needs for timely decision making made the use of exact techniques prohibitive. One of the crucial tasks to achieve good performance is the parameter tuning of metaheuristics. The parameter tuning is responsible for the efficiency of any metaheuristic algorithm [1]. One of the most difficult, yet not significantly explored, fields of metaheuristics research, is achieving optimal parameter tuning. An algorithm with properly tuned parameters converges to the global location faster, which increases the algorithm's efficiency even further [1]. While it has long been understood that a metaheuristic's efficiency is dependent on the values of its parameters, the parameter tuning issue was not formally addressed by the academic community until the late twentieth century [2]. Metaheuristics were manually tuned during the first few decades of study, i.e., by conducting experiments with various parameter values and choosing the best one [3]. The user would generally do this based on his own experience or by trial and error driven by rules of thumb. This process is not only time-consuming, but also prone to error, difficult to replicate and costly [4; 5]. In the last 20 years, the literature has increasingly emphasized the need for more systematic approaches to metaheuristic parameter tuning. As a result, researchers and end users have paid more attention to the parameter tuning issue, and more effort has been put into developing systematic and sophisticated approaches to address it [3]. Transferring part of the parameter tuning effort to the algorithm is one of the oldest and most important goals in the field of metaheuristics. The goal is to equip metaheuristics with intelligent mechanisms that enable them to respond to the problem or situation on their own [5]. Metaheuristics' parameter tuning can be efficiently performed using learning techniques, in order to free the user of that effort. The main motivation of the presented study arose from the need to propose and develop new approaches that are able to adaptively control, coordinate and optimize the solutions to the different challenges in optimizing the scheduling problems present in real production environments. This work was carried out with the aim of being integrated in the AutoDynAgents system [6; 7; 8; 9], which is a multi-agent system for the autonomous, distributed and cooperative resolution of scheduling problems subject to disruption. AutoDynAgents system uses metaheuristics for determining (near-)optimal scheduling plans. Once the system's environment is complex, dynamic and unpredictable, the learning issues become indispensable.