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A Genetic Local Search Algorithm for the Multiple Optimisation of the Balanced Academic Curriculum Problem

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Cutting-Edge Research Topics on Multiple Criteria Decision Making (MCDM 2009)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 35))

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Abstract

We deal with the Balanced Academic Curriculum Problem, a real world problem that is currently part of CSPLIB. We introduce a Genetic Local Search algorithm to solve this problem using two objectives which is a more realistic model than the one we used in our previous research. The tests carried out show that our algorithm obtains better solutions than systematic search techniques in the same amount of time.

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Author information

Authors and Affiliations

  1. Departamento de Informática, Universidad Técnica Federico Santa María Valparaíso, Chile

    Carlos Castro & Eric Monfroy

  2. Escuela de Informática, Pontificia Universidad Católica de Valparaíso Valparaíso, Chile

    Broderick Crawford

Authors
  1. Carlos Castro
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  2. Broderick Crawford
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  3. Eric Monfroy
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Nebraska, NE 68182, Omaha, USA

    Yong Shi

  2. Chinese Academy of Sciences, Beijing, P.R. China

    Shouyang Wang  & Jianping Li  & 

  3. University of Electronic Science and Technology of China, Chengdu, China

    Yi Peng  & Yong Zeng  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Castro, C., Crawford, B., Monfroy, E. (2009). A Genetic Local Search Algorithm for the Multiple Optimisation of the Balanced Academic Curriculum Problem. In: Shi, Y., Wang, S., Peng, Y., Li, J., Zeng, Y. (eds) Cutting-Edge Research Topics on Multiple Criteria Decision Making. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02298-2_119

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  • DOI: https://doi.org/10.1007/978-3-642-02298-2_119

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02297-5

  • Online ISBN: 978-3-642-02298-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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