A novel multi-objective grammar-based framework for the generation of Convolutional Neural Networks
作者：
Highlights：
• An evolutional framework that optimizes CNNs with no need of an expert is introduced.
• A context-free grammar responsible for modeling the architecture of CNNs is proposed.
• The framework is tested on 5 datasets, guided by two metrics (accuracy and F1-score).
• The results obtained are analyzed and compared to other known architectures.
• The framework is capable of generating models that surpass other known architectures.
摘要
•An evolutional framework that optimizes CNNs with no need of an expert is introduced.•A context-free grammar responsible for modeling the architecture of CNNs is proposed.•The framework is tested on 5 datasets, guided by two metrics (accuracy and F1-score).•The results obtained are analyzed and compared to other known architectures.•The framework is capable of generating models that surpass other known architectures.
论文关键词：Deep Neural Networks,Grammatical evolution,Multi-objective optimization
论文评审过程：Received 21 January 2022, Revised 25 July 2022, Accepted 21 August 2022, Available online 3 September 2022, Version of Record 16 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118670
