A novel technique to self-adapt parameters in parallel/distributed genetic programming
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- @Article{Russo:SC,
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author = "Marco Russo",
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title = "A novel technique to self-adapt parameters in
parallel/distributed genetic programming",
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journal = "Soft Computing",
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year = "2020",
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volume = "24",
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number = "22",
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pages = "16885--16894",
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month = nov,
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keywords = "genetic algorithms, genetic programming, ANN, Neural
networks, Evolutionary computing, Parallel computing,
Distributed computing",
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ISSN = "1432-7643",
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DOI = "doi:10.1007/s00500-020-04982-w",
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abstract = "This paper introduces the Supervisor Evolutionary
Algorithm, a novel technique that allows for self-adapt
almost all the internal parameters in parallel
distributed client-server genetic programming. This
novel adapting mechanism, is itself of an evolutionary
nature, so we have a double evolutionary tool. The
upper level, as is usual in evolutionary computing, has
its own customized selection, crossover, and mutation
mechanisms. The lower stage used here is the Brain
Project a parallel-distributed software tool for formal
modelling of numerical data using a hybrid
neural-genetic programming technique. As demonstrated
by the experiment reported in this paper, our approach
works well adapting continuously its internal
parameters.",
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notes = "Project web page:
http://superpippo.ct.infn.it/~marco/",
- }
Genetic Programming entries for
Marco Russo
Citations