The effect of the set of low-level heuristics on the performance of selection hyper-heuristics

  • Authors:
  • M. Mısır;K. Verbeeck;P. De Causmaecker;G. Vanden Berghe

  • Affiliations:
  • CODeS, KAHO Sint-Lieven, Belgium,CODeS, Department of Computer Science, KU Leuven Campus Kortrijk, Belgium;CODeS, KAHO Sint-Lieven, Belgium,CODeS, Department of Computer Science, KU Leuven Campus Kortrijk, Belgium;CODeS, Department of Computer Science, KU Leuven Campus Kortrijk, Belgium;CODeS, KAHO Sint-Lieven, Belgium,CODeS, Department of Computer Science, KU Leuven Campus Kortrijk, Belgium

  • Venue:
  • PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

The present study investigates the effect of heuristic sets on the performance of several selection hyper-heuristics. The performance of selection hyper-heuristics is strongly dependant on low-level heuristic sets employed for solving target problems. Therefore, the generality of hyper-heuristics should be examined across various heuristic sets. Unlike the majority of hyper-heuristics research, where the low-level heuristic set is considered given, the present study investigates the influence of the low-level heuristics on the hyper-heuristic's performance. To achieve this, a number of heuristic sets was generated for the patient admission scheduling problem by setting the parameters of a set of parametric heuristics with specific values. These values were set such that nine heuristic sets with different improvement capabilities, speed characteristics and size were generated. A group of hyper-heuristics with certain selection mechanisms and acceptance criteria having dissimilar intensification/diversification abilities were taken from the literature enabling a comprehensive analysis. The experimental results indicated that different hyper-heuristics perform superiorly on distinct heuristic sets. The results can be explained and hence result in hyper-heuristic design recommendations.