## 5 Results
### Topics  
The Python library has been used to identify key topics and recurring themes within the existing literature to gain deeper insights into the evolving landscape of hyper-heuristic research. Table 4 presents the results of the topic modeling analysis. It includes the number of documents assigned to each topic, the most relevant author keywords associated with the topic, and a suggested group title based on these keywords. Figure 5: Top 10 keyword relevance per year. The resulting topics in Table 4 offer a comprehensive overview of the current state of hyper-heuristic research. By examining these topics and their relationships to the word groups in the graph, we can uncover valuable insights into the dominant research areas and emerging trends in this field. The topic descriptions, based on the resulting keywords and the representative articles listed in Table 5, are the following:  
* **Topic 0**. _Scheduling and Production Optimization_ This topic centers on applying hyper-heuristics for solving complex scheduling and production planning problems, particularly in manufacturing contexts such as job shop and flow shop scheduling. The most representative studies address the design of scheduling rules and dispatching strategies using multi-objective optimization, genetic programming, and Q-learning to enhance energy efficiency and throughput. The representative keywords listed above underscore the practical orientation of this research area toward industrial applications, with a notable emphasis on rules-based systems and simulation-based optimization frameworks. * **Topic 1**. _Metaheuristic Optimization and Framework Development_ This topic focuses on designing and tuning generalized hyper-heuristic frameworks for combinatorial optimization problems. The dominant approach involves hybrid methods aimed at selecting or generating heuristics dynamically. Representative \begin{table}
\begin{tabular}{c p{142.3 pt} p{142.3 pt}} \hline \hline
**Suggested Group** & \multicolumn{2}{c}{**Associated Words**} \\ \hline _Scheduling and Production Optimization_ & Scheduling, programming, genetic, rules, shop, job, time, results, manufacturing, production & Topic 0 (177) \\ \hline _Metaheuristic Optimization and Framework Development_ & Optimization, level, search, selection, framework, results, approach, performance, low, different & Topic 1 (127) \\ \hline _Vehicle Routing and Logistics_ & Cost, routing, vehicle, model, logistics, carbon, time, traffic, results, level & Topic 2 (61) \\ \hline _Cloud Computing and Resource Management_ & Resource, cloud, energy, computing, scheduling, network, sensor, performance, task, consumption & Topic 3 (51) \\ \hline _Machine Learning and Data Mining_ & Classification, data, feature, learning, SVM, accuracy, decision, tree, dataset, machine & Topic 4 (42) \\ \hline _Educational Timetabling_ & Timetabling, exam, approach, research, graph, course, table, educational, ITC, instances & Topic 5 (31) \\ \hline _Traveling Salesman Problem_ & City, salesman, approach, traveling, facility, tour, solution, instances, search, neighborhood & Topic 6 (15) \\ \hline _Evolutionary Computation for Routing Problems_ & Routing, uncertain, policies, arc, genetic, programming, capacitated, policy, vehicles & Topic 7 (14) \\ \hline _Healthcare Scheduling and Resource Allocation_ & Rostering, patient, nurse, pas, hospital, care, approach, scheduling, solutions, crowd & Topic 8 (14) \\ \hline _Unmanned Aerial Vehicle (ULAV) Path Planning and Optimization_ & UAV, swarm, coverage, path, planning, optimal, optimization, DSO, environment, marine & Topic 9 (13) \\ \hline _Software Testing and Quality Assurance_ & Testing, software, strategies, generation, cases, SPL, coverage, pairwise, selection, approach & Topic 10 (13) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Topic Modeling Result and Suggested Group. studies explore hyper-heuristic architectures adaptable across domains, emphasizing performance analysis, algorithm generality, and reusing heuristic components within flexible solution frameworks.