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Erschienen in:
2017 | OriginalPaper | Buchkapitel
A Hybrid Evolutionary Algorithm based on Adaptive Mutation and Crossover for Collaborative Learning Team Formation in Higher Education
verfasst von : Virginia Yannibelli, Analía Amandi
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017
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Abstract
In this paper, we address a collaborative learning team formation problem in higher education environments. This problem considers a grouping criterion successfully evaluated in a wide variety of higher education courses and training programs. To solve the problem, we propose a hybrid evolutionary algorithm based on adaptive mutation and crossover processes. The behavior of these processes is adaptive according to the diversity of the evolutionary algorithm population. These processes are meant to enhance the evolutionary search. The performance of the hybrid evolutionary algorithm is evaluated on ten different data sets, and then, is compared with that of the best algorithm previously proposed in the literature for the addressed problem. The obtained results indicate that the hybrid evolutionary algorithm considerably outperforms the previous algorithm.