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Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework

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Abstract

This paper introduces a Grammar-based Genetic Programming Hyper-Heuristic framework (GPHH) for evolving constructive heuristics for timetabling. In this application GP is used as an online learning method which evolves heuristics while solving the problem. In other words, the system keeps on evolving heuristics for a problem instance until a good solution is found. The framework is tested on some of the most widely used benchmarks in the field of exam timetabling and compared with the best state-of-the-art approaches. Results show that the framework is very competitive with other constructive techniques, and did outperform other hyper-heuristic frameworks on many occasions.

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Authors and Affiliations

  1. Department of Computer Science, Loughborough University, Loughborough, UK

    Mohamed Bader-El-Den & Shaheen Fatima

  2. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Riccardo Poli

Authors
  1. Mohamed Bader-El-Den
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  2. Riccardo Poli
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  3. Shaheen Fatima
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Correspondence to Mohamed Bader-El-Den.

Additional information

The authors acknowledge financial support from EPSRC (grants EP/C523377/1 and EP/C523385/1).

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Bader-El-Den, M., Poli, R. & Fatima, S. Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework. Memetic Comp. 1, 205–219 (2009). https://doi.org/10.1007/s12293-009-0022-y

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