Hyper-heuristic approach: automatically designing adaptive mutation operators for evolutionary programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Hong:CIS,
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author = "Libin Hong and John R. Woodward and Ender Ozcan and
Fuchang Liu",
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title = "Hyper-heuristic approach: automatically designing
adaptive mutation operators for evolutionary
programming",
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journal = "Complex \& Intelligent Systems",
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year = "2021",
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volume = "7",
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number = "6",
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pages = "3135--3163",
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month = dec,
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keywords = "genetic algorithms, genetic programming,
Hyper-heuristic, Evolutionary programming, Adaptive
mutation",
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ISSN = "2198-6053",
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URL = "https://rdcu.be/cxGCh",
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DOI = "doi:10.1007/s40747-021-00507-6",
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size = "29 pages",
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abstract = "Genetic programming (GP) automatically designs
programs. Evolutionary programming (EP) is a
real-valued global optimisation method. EP uses a
probability distribution as a mutation operator, such
as Gaussian, Cauchy, or Levy distribution.This study
proposes a hyper-heuristic approach that employs GP to
automatically design different mutation operators for
EP. At each generation, the EP algorithm can adaptively
explore the search space according to historical
information. The experimental results demonstrate that
the EP with adaptive mutation operators, designed by
the proposed hyper-heuristics,exhibits improved
performance over other EP versions (both manually and
automatically designed). Many researchers in
evolutionary computation advocate adaptive search
operators (which do adapt over time) over non-adaptive
operators (which do not alter over time). The core
motive of this study is that we can automatically
design adaptive mutation operators that out perform
automatically designed non-adaptive mutation
operators.",
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notes = "Hangzhou Normal University, Hangzhou, China",
- }
Genetic Programming entries for
Libin Hong
John R Woodward
Ender Ozcan
Fuchang Liu
Citations