A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming
Created by W.Langdon from
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- @Article{Hong:2018:ASC,
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author = "Libin Hong and John H. Drake and John R. Woodward and
Ender Ozcan",
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title = "A hyper-heuristic approach to automated generation of
mutation operators for evolutionary programming",
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journal = "Applied Soft Computing",
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year = "2018",
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volume = "62",
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pages = "162--175",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Evolutionary
programming, Automatic design, Hyper-heuristics,
Continuous optimisation",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494617306051",
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DOI = "doi:10.1016/j.asoc.2017.10.002",
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size = "14 pages",
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abstract = "Evolutionary programming can solve black-box function
optimisation problems by evolving a population of
numerical vectors. The variation component in the
evolutionary process is supplied by a mutation
operator, which is typically a Gaussian, Cauchy, or
Levy probability distribution. In this paper, we use
genetic programming to automatically generate mutation
operators for an evolutionary programming system,
testing the proposed approach over a set of function
classes, which represent a source of functions. The
empirical results over a set of benchmark function
classes illustrate that genetic programming can evolve
mutation operators which generalise well from the
training set to the test set on each function class.
The proposed method is able to outperform existing
human designed mutation operators with statistical
significance in most cases, with competitive results
observed for the rest.",
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notes = "hyperheuristic. Supplement C. Also known as
\cite{HONG2018162}",
- }
Genetic Programming entries for
Libin Hong
John H Drake
John R Woodward
Ender Ozcan
Citations