## 3 Related Studies  
The literature review encompasses several studies focused on hyper-heuristics. Most of these works delve into specific problem domains, illustrating how hyper-heuristics can be applied to solve particular challenges. For instance, [PERSON] [9] concentrated their review on the application of hyper-heuristics to educational timetabling. Their study comprehensively analyzes existing research in this area, including the seminal studies by [PERSON] and [PERSON] [2; 3]. Another recent contribution by [PERSON] et al. [10] examined various heuristic methods (including hyper-heuristics) within the context of the vehicle routing problem. [PERSON] et al. [11] concentrated on scheduling problems, examining hyper-heuristic applications in this field over the past decade. Their work categorized hyper-heuristic strategies and configurations used in diverse scheduling scenarios, highlighting the prevalence of evolutionary computation and identifying areas for future research, such as multi-objective optimization. [PERSON] et al. [12] provided a foundational classification of hyper-heuristic variants based on the heuristic search space and feedback mechanisms employed during the learning process. This study established a cornerstone for the field, aiming to automate the development of computational search methodologies for operational research problems. A later revision of this study [8] further refined the classification and definition of hyper-heuristics, distinguishing between heuristic selection and heuristic generation while also highlighting recent research trends and expanding applications into new domains such as bioinformatics, game strategies, and software engineering. [PERSON] et al. [13] provided a review of reinforcement-learning-based hyper-heuristics. They categorize existing algorithms into value-based and policy-based approaches, describing typical algorithms in each category. The review also identifies current research gaps and suggests future directions for developing reinforcement-learning-based hyper-heuristics. [PERSON] et al. [14] analyzed hyper-heuristic research within the specific context of combinatorial optimization, investigating whether research efforts align with the most relevant problems in that domain. Their analysis identified key problem domains, such as shortest path and minimum spanning tree, suggesting areas for increased hyper-heuristic application. [PERSON] et al. [15] took a broader approach, employing text-mining techniques to analyze a large corpus of hyper-heuristic literature. Their study focused on identifying dominant research topics and visualizing relationships, revealing widespread manifestations of different hyper-heuristic areas. While these studies provide valuable insights into specific applications and trends within hyper-heuristics, this article aims to provide a broader analysis, examining the publications of hyper-heuristics across all areas and analyzing the publications according to different dimensions. To analyze this document set, we focused on the following research questions:  
* How has the hyper-heuristics research landscape evolved over time, as reflected in publication trends? * What key terms define the intellectual core of hyper-heuristics research? * How have the thematic priorities within hyper-heuristics research shifted over the years? * What are the dominant research themes and application areas within the hyper-heuristic domain? * Which countries and regions are at the forefront of hyper-heuristics research? * Who are the key thought leaders and influential researchers shaping the field of hyper-heuristics? * Which journals serve as the main outlets for disseminating cutting-edge hyper-heuristics research? How does this research contribute to existing bibliometric analyses? Table 1 presents a comparative overview of the analytical approaches employed in previous studies and this research.