Abstract
This paper proposes an evolutionary-based iterative local search hyper-heuristic approach called Iterated Search Driven by Evolutionary Algorithm Hyper-Heuristic (ISEA). Two versions of this algorithm, ISEA-chesc and ISEA-adaptive, that differ in the re-initialization scheme are presented. The performance of the two algorithms was experimentally evaluated on six hard optimization problems using the HyFlex experimental framework and the algorithms were compared with algorithms that took part in the CHeSC 2011 challenge. Achieved results are very promising, the ISEA-adaptive would take the second place in the competition. It shows how important for good performance of this iterated local search hyper-heuristic is the re-initialization strategy.
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Kubalík, J. (2012). Hyper-Heuristic Based on Iterated Local Search Driven by Evolutionary Algorithm. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_13
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DOI: https://doi.org/10.1007/978-3-642-29124-1_13
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