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

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