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

Abstract

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 Award	8 Nov 2018
Original language	English
Awarding Institution	Univerisity of Nottingham
Supervisor	John Cartlidge (Supervisor), Ender Özcan (Supervisor) & Ruibin Bai (Supervisor)