- author = "Libin Hong",
- title = "Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes",
- school = "University of Nottingham",
- year = "2018",
- address = "UK",
- keywords = "genetic algorithms, genetic programming",
- language = "en",
- bibsource = "OAI-PMH server at eprints.nottingham.ac.uk",
- oai = "oai:eprints.nottingham.ac.uk:52348",
-
URL = "
http://eprints.nottingham.ac.uk/52348/",
-
URL = "
http://eprints.nottingham.ac.uk/52348/1/THESIS_LATEST_12JUNE2018.pdf",
-
URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757549",
-
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.",
-
notes = "Supervisors: John Cartlidge, Ender Ozcan, Ruibin
Bai
uk.bl.ethos.757549 ISNI: 0000 0004 7430 3661",
