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A Hybrid Evolutionary Algorithm based on Adaptive Mutation and Crossover for Collaborative Learning Team Formation in Higher Education

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Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

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.

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Author information

Authors and Affiliations

  1. ISISTAN Research Institute, UNCPBA University, Campus Universitario, Paraje Arroyo Seco, 7000, Tandil, Argentina

    Virginia Yannibelli & Analía Amandi

  2. CONICET, National Council of Scientific and Technological Research, Buenos Aires, Argentina

    Virginia Yannibelli & Analía Amandi

Authors
  1. Virginia Yannibelli
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  2. Analía Amandi
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Corresponding author

Correspondence to Virginia Yannibelli .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Cite this paper

Yannibelli, V., Amandi, A. (2017). A Hybrid Evolutionary Algorithm based on Adaptive Mutation and Crossover for Collaborative Learning Team Formation in Higher Education. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_38

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  • DOI: https://doi.org/10.1007/978-3-319-68935-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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