Research Output

An improved immune inspired hyper-heuristic for combinatorial optimisation problems.

  The meta-dynamics of an immune-inspired optimisation sys- tem NELLI are considered. NELLI has previously shown to exhibit good performance when applied to a large set of optimisation problems by sustaining a network of novel heuristics. We address the mechanisms by which new heuristics are defined and subsequently generated. A new representation is defined, and a mutation-based operator inspired by clonal- selection introduced to control the balance between explo- ration and exploitation in the generation of new network elements. Experiments show significantly improved perfor- mance over the existing system in the bin-packing domain. New experiments in the job-scheduling domain further show the generality of the approach.

  • Date:

    12 July 2014

  • Publication Status:

    Published

  • Publisher

    ACM Digital Library

  • DOI:

    10.1145/2576768.2598241

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Sim, K. & Hart, E. (2014). An improved immune inspired hyper-heuristic for combinatorial optimisation problems. In Igel, C. (Ed.). Proceedings of GECCO 2014 (Genetic and Evolutionary Computation Conference), 121-128. doi:10.1145/2576768.2598241. ISBN 978-1-4503-2662-9

Authors

Keywords

Immune-inspired optimisation system; NELLI; novel heuristics;

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