Artificial Neural Network Development by means of Genetic Programming with Graph Codification

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@Article{Rivero:2005:ijamcs,

• author = "Daniel Rivero and Julian Dorado and Juan R. Rabunal and Alejandro Pazos and Javier Pereira",
• title = "Artificial Neural Network Development by means of Genetic Programming with Graph Codification",
• journal = "International Journal of Applied Mathematics and Computer Sciences",
• year = "2005",
• volume = "1",
• number = "1",
• pages = "41--46",
• month = "Winter",
• keywords = "genetic algorithms, genetic programming, Artificial Neural Networks, Evolutionary Computation",
• ISSN = "2070-3902",
• URL = "http://www.waset.org/journals/ijamcs/v1/v1-1-8.pdf",
• size = "6 pages",
• 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. This work presents a new technique that uses Genetic Programming (GP) for automatically generating ANNs. To do this, the GP algorithm had to be changed in order to work with graph structures, so ANNs can be developed. 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. The results of those comparisons show that the system achieves good results comparable with the already existing techniques and, in most of the cases, they worked better than those techniques.",

}

Genetic Programming entries for Daniel Rivero Cebrian Julian Dorado Juan Ramon Rabunal Dopico Alejandro Pazos Sierra Javier Pereira

Citations