# Identifying Hyper-Heuristic Trends through a Text Mining Approach on the Current Literature
## 5 Conclusions and Future Work  
As far as we are aware, this study is the first to explore text mining algorithms for the field of hyper-heuristics. We conducted brief research on 963 manuscripts published in journals and conferences written in English to explore potential topics in the area of hyper-heuristics. Traditional literature review systems are time-consuming and lack objectivity in the results. This motivated us to conduct this systematic literature review using text mining techniques, machine learning algorithms, and graphical representations to discover different trends related to hyper-heuristics. Therefore, we provided a practical example of text mining and clustering as an alternative solution to understand the literature and provide researchers with the tools to create strategic plans.  
As we stated earlier in this document, the objective of this work was to provide new researchers with valuable information for entering the hyper-heuristic research area. In the following lines, we summarize some relevant ideas that could be useful for starters in this research area:  
* This study revealed the increase in hyper-heuristics publications in the last three years (cf. Figure 2). About the analyzed journals, _Applied Soft Computing, Expert Systems with Applications_, and _Information Sciences_ are the most popular options for publications on hyper-heuristic related works. Considering the number of articles and citations, we found that the most relevant journals regarding hyper-heuristics are the _European Journal of Operation Research_ and _IEEE Transactions on Evolutionary Computation_ (cf. Figure 3).
* We noticed that publications concerning hyper-heuristics are strongly related to genetic and particle swarm algorithms. These techniques are widely used in other domains and associated with other terms (Tables 1 and 3). In addition, scheduling, when solved with hyper-heuristics, is the domain most often associated with other terms such as optimization, evolutionary algorithms, and timetabling (Table 4). We also observed that scheduling, along with optimization, is the most-used term in the word cloud (cf. Figure 4), which confirms its value for the hyper-heuristic community. It is important to highlight that many works that use hyper-heuristics for scheduling problems have focused on generation hyper-heuristics powered mainly by genetic programming.
* Three problem domains recurrently appeared in our clusters: scheduling (including job shop and timetabling), vehicle routing and constraint satisfaction. All but the last seem to remain active nowadays.  
Although this study provides a new perspective on hyper-heuristics, it has some limitations. First, we collected the data only from Scopus. Second, this work focused on journal and conference articles. Third, we mainly focused on the frequency of reports. Still, we did not consider only the most relevant studies for hyper-heuristics. Future work could address the limitations of collecting information for multiple data sources and analyze book chapters related to hyper-heuristics. We also remark on the need for a strategy to select relevant hyper-heuristic publications to better understand the distribution of concepts.  
Conceptualization, A.K.G.-E. and J.C.O.-B.; Formal analysis, A.K.G.-E.; Funding acquisition, A.K.G.-E.; Investigation, A.K.G.-E. and J.C.O.-B.; Methodology, A.K.G.-E., I.A., J.M.C.-D. and J.C.O.-B.; Project administration, H.T.-M. and J.C.O.-B.; Software, A.K.G.-E. and J.C.O.-B.; Supervision, J.C.O.-B.; Validation, I.A., J.M.C.-D. and J.C.O.-B.; Visualization, A.K.G.-E. and J.C.O.-B.; Writing--original draft, A.K.G.-E. and J.C.O.-B.; Writing--review & editing, I.A., J.M.C.-D., H.T.-M. and J.C.O.-B.. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tecnologico de Monterrey through its Fund for financing the publication of Scientific Articles.  
Not applicable.  
The authors declare no conflict of interest.