A Sequence-based Selection Hyper-heuristic Utilising a Hidden Markov Model

Ahmed Kheiri, Ed Keedwell
2015 Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO '15  
Selection hyper-heuristics are optimisation methods that operate at the level above traditional (meta-)heuristics. Their task is to evaluate low level heuristics and determine which of these to apply at a given point in the optimisation process. Traditionally this has been accomplished through the evaluation of individual or paired heuristics. In this work, we propose a hidden Markov model based method to analyse the performance of, and construct, longer sequences of low level heuristics to
more » ... l heuristics to solve difficult problems. The proposed method is tested on the well known hyper-heuristic benchmark problems within the CHeSC 2011 competition and compared with a large number of algorithms in this domain. The empirical results show that the proposed hyper-heuristic is able to outperform the current best-in-class hyper-heuristic on these problems with minimal parameter tuning and so points the way to a new field of sequence-based selection hyper-heuristics.
doi:10.1145/2739480.2754766 dblp:conf/gecco/KheiriK15 fatcat:h4ya4g2vazbfdo3mvja7t2xp44