A Genetic Programming approach to inductive inference
of chaotic series, with reference to Solomonoff
complexity, is presented. It consists in evolving a
population of mathematical expressions looking for the
'optimal' one that generates a given chaotic data
series. Validation is performed on the Logistic, the
Henon and the Mackey-Glass series. The method is shown
effective in obtaining the analytical expression of the
first two series, and in achieving very good results on
the third one.
%0 Conference Paper
%1 conf/sac/FalcoTCP05
%A De Falco, Ivan
%A Tarantino, Ernesto
%A Della Cioppa, Antonio
%A Passaro, A.
%B Proceedings of the 2005 ACM Symposium on Applied
Computing (SAC)
%C Santa Fe, New Mexico, USA
%D 2005
%E Haddad, Hisham
%E Liebrock, Lorie M.
%E Omicini, Andrea
%E Wainwright, Roger L.
%I ACM
%K Algorithms, Automatic Chaotic Experimentation, Inductive Programming, algorithms, genetic inference, programming, series
%P 957--958
%T Inductive inference of chaotic series by Genetic
Programming: a Solomonoff-based approach
%U http://doi.acm.org/10.1145/1066677.1066897
%X A Genetic Programming approach to inductive inference
of chaotic series, with reference to Solomonoff
complexity, is presented. It consists in evolving a
population of mathematical expressions looking for the
'optimal' one that generates a given chaotic data
series. Validation is performed on the Logistic, the
Henon and the Mackey-Glass series. The method is shown
effective in obtaining the analytical expression of the
first two series, and in achieving very good results on
the third one.
%@ 1-58113-964-0
@inproceedings{conf/sac/FalcoTCP05,
abstract = {A Genetic Programming approach to inductive inference
of chaotic series, with reference to Solomonoff
complexity, is presented. It consists in evolving a
population of mathematical expressions looking for the
'optimal' one that generates a given chaotic data
series. Validation is performed on the Logistic, the
Henon and the Mackey-Glass series. The method is shown
effective in obtaining the analytical expression of the
first two series, and in achieving very good results on
the third one.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Santa Fe, New Mexico, USA},
author = {{De Falco}, Ivan and Tarantino, Ernesto and {Della Cioppa}, Antonio and Passaro, A.},
bibdate = {2006-02-10},
bibsource = {DBLP,
http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCP05},
biburl = {https://www.bibsonomy.org/bibtex/2bd329714a678da4011dd3c9a3d316e3a/brazovayeye},
booktitle = {Proceedings of the 2005 ACM Symposium on Applied
Computing (SAC)},
editor = {Haddad, Hisham and Liebrock, Lorie M. and Omicini, Andrea and Wainwright, Roger L.},
interhash = {4a21149fe0619dc589050933dbc8b10d},
intrahash = {bd329714a678da4011dd3c9a3d316e3a},
isbn = {1-58113-964-0},
keywords = {Algorithms, Automatic Chaotic Experimentation, Inductive Programming, algorithms, genetic inference, programming, series},
month = {March 13-17},
organisation = {ACM},
pages = {957--958},
publisher = {ACM},
size = {2 pages},
timestamp = {2008-06-19T17:38:30.000+0200},
title = {Inductive inference of chaotic series by Genetic
Programming: a Solomonoff-based approach},
url = {http://doi.acm.org/10.1145/1066677.1066897},
year = 2005
}