New Insights Into Diversification of Hyper-Heuristics
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- @Article{Ren:2014:ieeeCybernetics,
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author = "Zhilei Ren and He Jiang and Jifeng Xuan and Yan Hu and
Zhongxuan Luo",
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title = "New Insights Into Diversification of
Hyper-Heuristics",
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journal = "IEEE Transactions on Cybernetics",
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year = "2014",
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volume = "44",
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number = "10",
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month = oct,
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pages = "1747--1761",
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keywords = "genetic algorithms, genetic programming,
Hyper-heuristics, Ising spin glass, instance
perturbation, linear genetic programming, p-median",
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DOI = "doi:10.1109/TCYB.2013.2294185",
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ISSN = "2168-2267",
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abstract = "There has been a growing research trend of applying
hyper-heuristics for problem solving, due to their
ability of balancing the intensification and the
diversification with low level heuristics.
Traditionally, the diversification mechanism is mostly
realised by perturbing the incumbent solutions to
escape from local optima. In this paper, we report our
attempt toward providing a new diversification
mechanism, which is based on the concept of instance
perturbation. In contrast to existing approaches, the
proposed mechanism achieves the diversification by
perturbing the instance under solving, rather than the
solutions. To tackle the challenge of incorporating
instance perturbation into hyper-heuristics, we also
design a new hyper-heuristic framework HIP-HOP
(recursive acronym of HIP-HOP is an instance
perturbation-based hyper-heuristic optimisation
procedure), which employs a grammar guided high level
strategy to manipulate the low level heuristics. With
the expressive power of the grammar, the constraints,
such as the feasibility of the output solution could be
easily satisfied. Numerical results and statistical
tests over both the Ising spin glass problem and the
p-median problem instances show that HIP-HOP is able to
achieve promising performances. Furthermore, run time
distribution analysis reveals that, although being
relatively slow at the beginning, HIP-HOP is able to
achieve competitive solutions once given sufficient
time.",
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notes = "Also known as \cite{6690192}",
- }
Genetic Programming entries for
Zhilei Ren
He Jiang
Jifeng Xuan
Yan Hu
Zhongxuan Luo
Citations