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

• Published in: Genetic programming and evolvable machines Vol. 23; no. 3; pp. 427 - 452
• Main Authors: Lima, Ricardo H. R., Magalhães, Dimmy, Pozo, Aurora, Mendiburu, Alexander, Santana, Roberto
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022; Springer Nature B.V
• Subjects: 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

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...