A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures

Author:

Diniz Jessica Barbosa,Cordeiro Filipe R.,Miranda Pericles B. C.,Da Silva Laura A. Tomaz

Abstract

Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.

Publisher

Sociedade Brasileira de Computação - SBC

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A genetic programming approach to the automated design of CNN models for image classification and video shorts creation;Genetic Programming and Evolvable Machines;2024-03-14

2. Introduction;Adaptation, Learning, and Optimization;2021

3. Evolutionary Computation and Genetic Programming;Adaptation, Learning, and Optimization;2021

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