scholarly journals Grammar-based genetic programming for timetabling

2009 ◽  
Author(s):  
Mohamed Bader El Den ◽  
Riccardo Poli
Keyword(s):  
Genetic Programming ◽  
Grammar Based Genetic Programming

scholarly journals Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Rafael V. Veiga ◽  
Helio J. C. Barbosa ◽  
Heder S. Bernardino ◽  
João M. Freitas ◽  
Caroline A. Feitosa ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Asthma And Allergy ◽  
Grammar Based Genetic Programming

Grammar Based Genetic Programming for Software Configuration Problem

2017 ◽  
pp. 130-136 ◽  
Author(s):  
Fitsum Meshesha Kifetew ◽  
Denisse Muñante ◽  
Jesús Gorroñogoitia ◽  
Alberto Siena ◽  
Angelo Susi ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Software Configuration ◽  
Grammar Based Genetic Programming ◽  
Configuration Problem

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

2018 ◽  
Author(s):  
Jessica Barbosa Diniz ◽  
Filipe R. Cordeiro ◽  
Pericles B. C. Miranda ◽  
Laura A. Tomaz Da Silva
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Genetic Programming ◽  
Convolutional Neural Network ◽  
Research Area ◽  
Classification Performance ◽  
Programming Approach ◽  
Network Architectures ◽  
Neural Network Architectures ◽  
Grammar Based Genetic Programming

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.

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