Automatic generation of neural networks with structured Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Assuncao:2017:CEC,

• author = "Filipe Assuncao and Nuno Lourenco and Penousal Machado and Bernardete Ribeiro",
• booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Automatic generation of neural networks with structured Grammatical Evolution",
• year = "2017",
• pages = "1557--1564",
• abstract = "The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANN. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution",
• DOI = "doi:10.1109/CEC.2017.7969488",
• month = jun,
• notes = "Also known as \cite{7969488}",

}

Genetic Programming entries for Filipe Assuncao Nuno Lourenco Penousal Machado Bernardete Ribeiro

Citations