Automatic Generation of Neural Networks with Structured Grammatical Evolution

Authors

Filipe Assunção
Nuno Lourenço
Penousal Machado
Bernardete Ribeiro

Abstract

The effectiveness of Artificial Neural Networks (ANNs) depends on a non-trivial manual crafting of their topology and parameters. Typically, practitioners resort to a time consuming methodology of trial-and-error to find and/or adjust the models to solve specific tasks. To minimise this burden one might resort to algorithms for the automatic selection of the most appropriate properties of a given ANNs. A remarkable example of such methodologies is Grammar-based Genetic Programming. This work analyses and compares the use of two grammar-based methods, Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), to automatically design and configure ANNs. The evolved networks are used to tackle several classification datasets. Experimental results show that SGE is able to automatically build better models than GE, and that are competitive with the state of the art, outperforming hand-designed ANNs in all the used benchmarks.

Conference

IEEE Congress on Evolutionary Computation (CEC) 2017

Cited by

Year 2018 : 1 citations

Itano, F., de Sousa, M. A. D. A., & Del-Moral-Hernandez, E. (2018, July). Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.