A novel technique to self-adapt parameters in parallel/distributed genetic programming

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@Article{Russo:SC,

• author = "Marco Russo",
• title = "A novel technique to self-adapt parameters in parallel/distributed genetic programming",
• journal = "Soft Computing",
• year = "2020",
• volume = "24",
• number = "22",
• pages = "16885--16894",
• month = nov,
• keywords = "genetic algorithms, genetic programming, ANN, Neural networks, Evolutionary computing, Parallel computing, Distributed computing",
• ISSN = "1432-7643",
• DOI = "doi:10.1007/s00500-020-04982-w",
• 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.",
• notes = "Project web page: http://superpippo.ct.infn.it/~marco/",

}

Genetic Programming entries for Marco Russo

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