Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{Hong:thesis,
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author = "Libin Hong",
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title = "Hyper-heuristic approaches to automatically designing
heuristics as mutation operators for evolutionary
programming on function classes",
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school = "University of Nottingham",
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year = "2018",
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address = "UK",
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keywords = "genetic algorithms, genetic programming",
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language = "en",
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bibsource = "OAI-PMH server at eprints.nottingham.ac.uk",
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oai = "oai:eprints.nottingham.ac.uk:52348",
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URL = "http://eprints.nottingham.ac.uk/52348/",
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URL = "http://eprints.nottingham.ac.uk/52348/1/THESIS_LATEST_12JUNE2018.pdf",
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URL = "https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.757549",
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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.",
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notes = "Supervisors: John Cartlidge, Ender Ozcan, Ruibin
Bai
uk.bl.ethos.757549 ISNI: 0000 0004 7430 3661",
- }
Genetic Programming entries for
Libin Hong
Citations