Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes

  • Libin HONG

Student thesis: PhD Thesis


A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances. Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming.
Date of Award8 Nov 2018
Original languageEnglish
Awarding Institution
  • Univerisity of Nottingham
SupervisorJohn Cartlidge (Supervisor), Ender Özcan (Supervisor) & Ruibin Bai (Supervisor)


  • hyper-heuristic
  • evolutionary programming

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