The automatic design of parameter adaptation techniques for differential evolution with genetic programming
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- @Article{STANOVOV:2022:KBS,
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author = "Vladimir Stanovov and Shakhnaz Akhmedova and
Eugene Semenkin",
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title = "The automatic design of parameter adaptation
techniques for differential evolution with genetic
programming",
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journal = "Knowledge-Based Systems",
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volume = "239",
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pages = "108070",
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year = "2022",
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ISSN = "0950-7051",
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DOI = "doi:10.1016/j.knosys.2021.108070",
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URL = "https://www.sciencedirect.com/science/article/pii/S0950705121011461",
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keywords = "genetic algorithms, genetic programming, Differential
evolution, Parameter adaptation",
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abstract = "This study proposes a technique aimed at the automatic
search for parameter adaptation strategies in a
differential evolution algorithm with genetic
programming symbolic regression. Genetic programming is
applied to find the symbolic expression for scaling
factor control during the optimization process of
differential evolution based on the current
computational resource, ratio of successful solutions
and adapted scaling factor value. The design of the
parameter adaptation technique is performed by a
computational experiment, which consisted in solving
several complex optimization problems. Better symbolic
expressions are selected with regards to the Friedman
ranking procedure, and the best solutions are
additionally evaluated to compare them to the existing
parameter adaptation techniques. The experimental
results show that the automatically designed parameter
adaptation techniques described by symbolic expressions
are capable of outperforming existing parameter
adaptation methods, while using different information
sources. The analysis of automatically generated
solutions shows that the proposed technique can be
considered an automatic knowledge extraction method.
This is due to the results showing that well-performing
parameter adaptation can behave differently from
state-of-the-art methods, thereby revealing previously
unknown algorithm properties",
- }
Genetic Programming entries for
Vladimir Stanovov
Shakhnaz Akhmedova
Eugene Semenkin
Citations