A novel group recommender system for domain-independent decision support customizing a grouping genetic algorithm

Abstract

AbstractGroup formation is a complex task requiring computational support to succeed. In the literature, there has been considerable effort in the development of algorithms for composing groups as well as their evaluation. The most widely used approach is the Genetic Algorithm, as, it can handle numerous variables, generating optimal solutions according to the problem requirements. In this study, a novel genetic algorithm was developed for forming groups using innovative genetic operators, such as a modification of 1-point and 2-point crossover, the gene and the group crossover, to improve its performance and accuracy. Moreover, the proposed algorithm can be characterized as domain-independent, as it allows any input regardless of the domain problem; i.e., whether the groups concern objects, items or people, or whether the field of application is industry, education, healthcare, etc. The grouping genetic algorithm has been evaluated using a dataset from the literature in terms of its settings, showing that the tournament selection is better to be chosen when a quick solution is required, while the introduced gene and group crossover operators are superior to the classic ones. Furthermore, the combination of up to three crossover operators is ideal solution concerning algorithm’s accuracy and execution time. The effectiveness of the algorithm was tested in two grouping cases based on its acceptability. Both the students participated in forming collaborative groups and the professors participated in evaluating the groups of courses created were highly satisfied with the results. The contribution of this research is that it can help the stakeholders achieve an effective grouping using the presented genetic algorithm. In essence, they have the flexibility to execute the genetic algorithm in different contexts as many times as they want until to succeed the preferred output by choosing the number of operators for either greater accuracy or reduced execution time.