Publicacións

@article{Rivero200794,

title = {A comparison between ANN generation and training methods and their development by means of graph evolution: 2 sample problems},

author = {Daniel Rivero and Julián Dorado and Juan R. Rabuñal-Dopico and Marcos Gestal},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38049110614&doi=10.1007%2f978-3-540-73007-1_12&partnerID=40&md5=b9b32d8e81c8a0a77de0c1b8aa62d788},

doi = {10.1007/978-3-540-73007-1_12},

issn = {03029743},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {4507 LNCS},

pages = {94-101},

publisher = {Springer Verlag},

abstract = {This paper presents a study in which a new technique for automatically developing Artificial Neural Networks (ANNs) by means of Evolutionary Computation (EC) tools is compared with the traditional evolutionary techniques used for ANN development. The technique used here is based on network encoding on graphs and also their performance and evolution. For this comparison, 2 different real-world problems have been solved using various tools, and the results are presented here. According to them, the results obtained with this technique can beat those obtained with other ANN development tools. © Springer-Verlag Berlin Heidelberg 2007.},

note = {cited By 0},

keywords = {Computer aided software engineering; Evolutionary algorithms; Graph theory; Problem solving; Real time systems, Development tools; Network encoding, Neural networks},

pubstate = {published},

tppubtype = {article}

}