Model approach to grammatical evolution: deep-structured analyzing of model and representation
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- @Article{He:2016:SC,
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author = "Pei He and Zelin Deng and Chongzhi Gao and
Xiuni Wang and Jin Li2",
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title = "Model approach to grammatical evolution:
deep-structured analyzing of model and representation",
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journal = "Soft Computing",
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year = "2017",
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volume = "21",
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number = "18",
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pages = "5413--5423",
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month = sep,
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, finite state automaton, model",
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ISSN = "1433-7479",
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URL = "https://rdcu.be/drca7",
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DOI = "doi:10.1007/s00500-016-2130-1",
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size = "11 pages",
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abstract = "Grammatical evolution (GE) is a combination of genetic
algorithm and context-free grammar, evolving programs
for given problems by breeding candidate programs in
the context of a grammar using genetic operations. As
far as the representation is concerned, classical GE as
well as most of its existing variants lacks awareness
of both syntax and semantics, therefore having no
potential for parallelism of various evaluation
methods. To this end, we have proposed a novel approach
called model-based grammatical evolution (MGE) in terms
of grammar model (a finite state transition system)
previously. It is proved, in the present paper, through
theoretical analysis and experiments that semantic
embedded syntax taking the form of regex (regular
expression) over an alphabet of simple cycles and paths
provides with potential for parallel evaluation of
fitness, thereby making it possible for MGE to have a
better performance in coping with more complex problems
than most existing GEs.",
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notes = "School of Computer Science and Educational Software,
Guangzhou University, Guangzhou, 510006, China",
- }
Genetic Programming entries for
Pei He
Zelin Deng
Chongzhi Gao
Xiuni Wang
Jin Li2
Citations