Hyper-Heuristic Based on Iterated Local Search Driven by Evolutionary Algorithm

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, ISEAchesc 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 [4] and the algorithms were compared with algorithms that took part in the CHeSC 2011 challenge [10]. 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.