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

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 throu...

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Vydáno v:Genetic programming and evolvable machines Ročník 23; číslo 3; s. 427 - 452
Hlavní autoři: Lima, Ricardo H. R., Magalhães, Dimmy, Pozo, Aurora, Mendiburu, Alexander, Santana, Roberto
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2022
Springer Nature B.V
Témata:
Artificial Intelligence
Artificial neural networks
Biomedical Engineering and Bioengineering
Compilers
Computer Science
Configuration management
Electrical Engineering
Evolutionary algorithms
Genetic algorithms
Image classification
Image segmentation
Interpreters
Machine learning
Neural networks
Object recognition
Programming Languages
Programming Techniques
Software Engineering/Programming and Operating Systems
Special Issue: Highlights of Genetic Programming 2021 Events
Speech recognition
Grammatical evolution
Automatic design
Deep neural networks
Evolutionary algorithms
Genetic programming
ISSN:1389-2576, 1573-7632
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Shrnutí: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. In this work, 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.
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ISSN:1389-2576
1573-7632
DOI:10.1007/s10710-022-09432-0