A grammar-based GP approach applied to the design of deep neural networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@Article{Lima:2022:GPEM,

• author = "Ricardo H. R. Lima and Dimmy Magalhaes and Aurora Pozo and Alexander Mendiburu and Roberto Santana",
• title = "A grammar-based {GP} approach applied to the design of deep neural networks",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2022",
• volume = "23",
• number = "3",
• pages = "427--452",
• month = sep,
• note = "Special Issue: Highlights of Genetic Programming 2021 Events",
• keywords = "genetic algorithms, genetic programming, Grammatical evolution, ANN, Evolutionary algorithms, Automatic design, Deep neural networks",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-022-09432-0",
• abstract = "Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional Neural Networks for image classification, Graph Neural Networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.",
• notes = "Department of Computer Science, Federal University of Parana, Curitiba, Parana, Brazil",

}

Genetic Programming entries for Ricardo Henrique Remes de Lima Dimmy Magalhaes Aurora Trinidad Ramirez Pozo Alexander Mendiburu Roberto Santana

Citations