Modifying genetic programming for artificial neural network development for data mining

Authors:
Daniel Rivero;Julián Dorado;Juan R. Rabuñal;Alejandro Pazos
Affiliations:
University of A Coruña, Fac. Informatica, Campus Elviña, 15071, A Coruña, Spain;University of A Coruña, Fac. Informatica, Campus Elviña, 15071, A Coruña, Spain;University of A Coruña, Fac. Informatica, Campus Elviña, 15071, A Coruña, Spain;University of A Coruña, Fac. Informatica, Campus Elviña, 15071, A Coruña, Spain
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Year:
2008

Citing 0
Cited 5

A Genetic Algorithm for ANN Design, Training and Simplification

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Evolving simple feed-forward and recurrent ANNs for signal classification: a comparison

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Two layered Genetic Programming for mixed-attribute data classification

Applied Soft Computing
A framework for optimization of genetic programming evolved classifier expressions using particle swarm optimization

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Lazy learning for multi-class classification using genetic programming

ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

The development of artificial neural networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. However, there are some tools that provide the ability of automatically developing ANNs, many of them using evolutionary computation (EC) tools. One of the main problems of these techniques is that ANNs have a very complex structure, which makes them very difficult to be represented and developed by these tools. This work presents a new technique that modifies genetic programming (GP) so as to correctly and efficiently work with graph structures in order to develop ANNs. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of evolutionary computation (EC) techniques, several tests were performed with problems based on some of the most used test databases in the Data Mining domain. These comparisons show that the system achieves good results that are not only comparable to those of the already existing techniques but, in most cases, improve them.