## 1 Introduction  
Choosing a meta-heuristic to solve a particular optimization problem is a non-trivial task. Without detailed prior information as to which particular algorithm to use, it demands the evaluation of several algorithms in order to find out which is more suitable to solve a given problem. However, due to the non-deterministic nature of these algorithms, this process demands to be repeated several times. Hyper-heuristics, which are _heuristics to choose heuristics_, have been proposed as a means to both simplify and improve algorithm selection or configuration for optimization problems [1, 2]. The idea is, through automation of the heuristic search, to provide effective and reusable cross-domain search methodologies that are applicable to the problems with different characteristics from various domains without requiring much expert involvement [3]. Hyper-heuristics (HH) employ learning methods, by using some feedback from the search process. Based on the source of this feedback, HHs can be classified as online or offline. A hyper-heuristic employs online learning if learning takes place while the algorithm is solving an instance of a problem. It is offline if the knowledge is gathered in the form of rules or programs from a set of training instances that hopefully generalize to solving unseen instances [1; 2]. Hyper-heuristics can also be classified as selection or generation methodologies [4; 5]. Selection hyper-heuristics at the high-level control and mix low-level (meta)heuristics (LLHs), automatically deciding which one(s) to apply to the candidate solution(s) at each decision point of the iterative search process [4]. On the other hand, generation methodologies produce new heuristics or heuristic parts using pre-defined components. Much of the previous research focuses on online selection hyper-heuristics. The interest in such algorithms has been growing in recent years, but the majority of research in this area has been limited to single-objective optimization [6]. The majority of the research in the HH literature focus on treating operators, such as crossover, mutation, and differential evolution as LLH [2; 6]. In [7], [PERSON] et al. proposed the _Choice Function_, an equation responsible for rank heuristics considering the algorithm performance, in this case, a fitness function for a mono-objective problem, and the computational time. In [8], [PERSON] et al. introduced FFRMAB, a variation of the original Multi-Armed Bandit [9] (MAB) where the _Fitness Rate Ranking_ (_FRR_) was proposed as reward assignment. In this hyper-heuristic, a set of Differential Evolution operators was considered as LLH for being chosen. Following the choice of DE operators, [PERSON] et al. [10] proposed a hyper-heuristic based on the choice function to control a set of five Differential Evolution operators. Results showed this hyper-heuristic overcoming the performance of the standard MOEA (using a single operator) when solving ten unconstrained benchmark functions with two and three objectives. In [11], the authors applied a similar approach using several versions of MAB instead of using a Choice Function. In this case, the CEC 2009 benchmark [12] was employed for performance evaluation. In [13], [PERSON] et al. evaluated three different versions of MAB by applying them to the permutation flow shop problem. In [14], [PERSON] et al. designed a hyper-heuristic to solve the Class Integration Test Order Problem [15], a software engineering problem where nodes from a graph have to be visited, where these nodes are the classes to be tested. This hyper-heuristic was built using a choice function [6] and a Multi-Armed Bandit [9] to select a LLH from a set of nine to operate together with a fixed MOEA, in this case, the NSGA-II algorithm. This set was built by combining different crossover and mutation operators. The evaluation of LLHs was performed based on the dominance relationship among parent solutions and their offspring. In [16], this approach was tested considering SPEA2 as the fixed MOEA.