Genetic Programming for Inductive Inference of Chaotic Series

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{conf/wilf/FalcoCPT05,

• title = "Genetic Programming for Inductive Inference of Chaotic Series",
• author = "Ivan {De Falco} and Antonio {Della Cioppa} and A. Passaro and Ernesto Tarantino",
• year = "2005",
• pages = "156--163",
• editor = "Isabelle Bloch and Alfredo Petrosino and Andrea Tettamanzi",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "3849",
• booktitle = "Fuzzy Logic and Applications, 6th International Workshop, WILF 2005, Revised Selected Papers",
• address = "Crema, Italy",
• month = sep # " 15-17",
• bibdate = "2006-02-22",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/wilf/wilf2005.html#FalcoCPT05",
• keywords = "genetic algorithms, genetic programming, Solomonoff complexity, chaotic series",
• ISBN = "3-540-32529-8",
• DOI = "doi:10.1007/11676935_19",
• size = "8 pages",
• abstract = "In the context of inductive inference Solomonoff complexity plays a key role in correctly predicting the behavior of a given phenomenon. Unfortunately, Solomonoff complexity is not algorithmically computable. This paper deals with a Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, that consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given series of chaotic data. Validation is performed on the Logistic, the Henon and the Mackey-Glass series. The results show that the method is effective in obtaining the analytical expression of the first two series, and in achieving a very good approximation and forecasting of the Mackey-Glass series.",

}

Genetic Programming entries for Ivanoe De Falco Antonio Della Cioppa A Passaro Ernesto Tarantino

Citations