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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 todoi:10.1145/2739480.2754766 dblp:conf/gecco/KheiriK15 fatcat:h4ya4g2vazbfdo3mvja7t2xp44