## 5 Results
### Keywords Analysis  
Figure 4 illustrates the prominent research areas within hyper-heuristics. The graph highlights optimization problems, particularly scheduling and routing. The range of keywords indicates ongoing exploration of optimization techniques and the potential for hybrid approaches. Based on the frequency of these keywords in published works, we can identify the following thematic clusters:  
* Optimization Problems (with a particular focus on scheduling and routing)
* Job Shop Scheduling. A classic manufacturing problem involving multiple machines and jobs. \begin{table}
\begin{tabular}{c c} \hline
**Metric** & **Value** \\ \hline Timespan & 2003–2025 \\ Total Number of Countries & 68 \\ Total Number of Sources & 288 \\ \hline Total Number of Documents & 767 \\ Average Documents per Author & 1.6 \\ Average Documents per Source & 2.66 \\ Average Documents per Year & 34.86 \\ \hline Total Number of Authors & 1744 \\ Total Number of Author Keywords & 1797 \\ Total Single-Authored Documents & 17 \\ Total Multi-Authored Documents & 750 \\ Average Collaboration Index & 3.64 \\ Max H-Index & 23 \\ \hline Total Number of Citations & 17,861 \\ Average Citations per Author & 10.24 \\ Average Citations per Document & 23.29 \\ Average Citations per Source & 62.02 \\ \hline \end{tabular}
\end{table} 
Table 3: Preliminary statistical report. 2. Flexible Job Shop. A variant of job shop scheduling where machines can be assigned to different tasks. 3. Vehicle Routing Problem. Optimizing routes for vehicles to serve multiple customers efficiently. 4. Location Routing Problem. Determining optimal locations for facilities and routes for vehicles. 5. Traveling Salesman Problem. Finding the shortest route that visits all cities exactly once. These keywords collectively highlight the prevalent focus on optimization problems related to scheduling, routing, and logistics. Hyper-heuristics have proven their effectiveness in addressing the complexity and dynamism of these problems. * Intelligent Optimization Algorithms
* Particle Swarm Optimization. A metaheuristic inspired by the behavior of bird flocks and fish schools. * Genetic Programming. A genetic algorithm that evolves computer programs to solve problems. * Ant Colony Optimization. A metaheuristic inspired by the behavior of ants searching for food. These keywords emphasize the exploration and application of various optimization algorithms within the hyper-heuristic framework. Researchers have investigated the effectiveness of these algorithms as hyper-heuristics for specific problem domains. Figure 5 shows the most frequently occurring author keywords related to hyper-heuristics from 2020 to 2024. The figure reveals several key trends in the field. A prominent association is observed between hyper-heuristics and genetic programming. The consistent presence of genetic programming in hyper-heuristic publications over the past five years is primarily due to its utility as a method within hyper-heuristic frameworks. Genetic programming is frequently used to automatically generate new, problem-specific heuristics, which is a core aspect of hyper-heuristic methodologies. Additionally, genetic programming can be employed to select or combine existing heuristics, essentially acting as the hyper-heuristic itself. Furthermore, genetic programming serves as a standard benchmark for comparing the performance of novel hyper-heuristic approaches, and it can also be used in developing and optimizing hyper-heuristic frameworks, highlighting its continued relevance and applicability in this field. As expected, optimization topics dominate the landscape of hyper-heuristic algorithm applications. Figure 4 depicts the diversity and evolution of optimization-related research within this field. Over the years, hyper-heuristics have been employed to address a wide range of problems, including _combinatorial optimization_ and _multi-objective optimization_. The recurrent appearance of _metaheuristic_ underlines its fundamental role in the development of hyper-heuristics.