## 1 Introduction
For example, Genetic Programming has been used to redefine existing solvers, as evidenced by [PERSON] et al. in [15]. In their work, the authors focused on the well-known Particle Swarm Optimisation and obtained improved results over a dataset of 60 standard continuous problems. Nonetheless, their approach is not easy to extend to other metaheuristics. Hence, a significant effort would be required to generalise it. Another path where research has focused is on ways to combine different solvers. This is known as hybridisation [16; 17]. Bear in mind that hybrids may extend beyond the realm of metaheuristics. Thus, they may also integrate other approaches, e.g., Nelder-Mead Simplex [18]. Despite this, hybrids fall within the umbrella of metaheuristics. For instance, [PERSON] and [PERSON] used a Genetic Algorithm for automating the design of hybrid metaheuristics [19]. There are more examples that tune operators for a metaheuristic mould [20; 21]. However, we do not delve into them for the sake of brevity. We must mention, nonetheless, that there is another enticing strategy that transcends the field of metaheuristics. Such an approach is commonly known as Hyper-Heuristics (HHs), and they strive to provide a higher-level solver [22]. They do this by using the available heuristics in different ways. Thus, HHs stand as a broad field with different variants [23]. Amongst them reside selection hyper-heuristics, which combine the available heuristics into a procedure for solving a given optimisation problem. This approach has been tested on the generation of metaheuristics based on two or more simple heuristics, where authors have achieved an excellent performance in particular problems [24]. However, the authors failed to provide a concrete mathematical model so that further systematic research on metaheuristics could be pursued. In the previous lines, we have commented about the exacerbation of 'novel' metaheuristic proposals that lack a proper justification. [PERSON] et al. warned about this issue and its risks [6]. Still, remember that the NFL theorem reigns supreme, and so the quest for better solvers cannot halt. As such, a track worth pursuing deals with finding a way for combining different approaches, and different parameter settings, into a more robust solver. There are, indeed, some proposals for fusing metaheuristics, as we just mentioned. However, often times, such approaches are not easy to generalise. Moreover, there is no common ground regarding terminology and operations of metaheuristics, as most of them relate to the associated metaphor. Thus, it is essential to establish a standardised model, e.g., by using a generalised metaheuristic mould. We have taken a preliminary step in this direction by proposing a modelling scheme that allows merging Search Operators (SOs) from different metaheuristics [7]. Even so, it may be worthwhile to set up a branched model with different paths in such a way that different sets of SOs are applied to a given problem state to provide a more diverse behaviour. It may also be advantageous to extend the looping scheme so that the sequence of SOs can change throughout the solution process. This, however, is not a straightforward task with the previously proposed model. Therefore, in this work we extend upon the model and, thus, this manuscript offers five major contributions:  
1. Provides a broader metaheuristic model that allows for more complex structures, such as those containing topologies or sub-populations;
2. Presents an overview of the effect of having long sequences of heterogeneous search operators that can be applied to continuous optimisation problems;
3. Shows the effect of three features (differentiability, separability, and unimodality) over the performance of the generated metaheuristics;
4. Studies the nature of search operators selected when solving problems with different characteristics and dimensionalities;
5. Stipulates a hyper-heuristic model for furnishing metaheuristics that can outperform standard ones in a majority of scenarios.