Parsimonious genetic programming for complex process intelligent modeling: algorithm and applications
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Wei:2010:NCA,
-
title = "Parsimonious genetic programming for complex process
intelligent modeling: algorithm and applications",
-
author = "Xunkai Wei and Yinghong Li and Yue Feng",
-
journal = "Neural Computing and Applications",
-
year = "2010",
-
number = "2",
-
volume = "19",
-
pages = "329--335",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "0941-0643",
-
publisher = "Springer London",
-
DOI = "doi:10.1007/s00521-009-0308-5",
-
size = "7 pages",
-
abstract = "A novel genetic programming (GP) algorithm called
parsimonious genetic programming (PGP) for complex
process intelligent modeling was proposed. First, the
method uses traditional GP to generate nonlinear
input-output model sets that are represented in a
binary tree structure according to special
decomposition method. Then, it applies orthogonal least
squares algorithm (OLS) to estimate the contribution of
the branches, which refers to basic function term that
cannot be decomposed anymore, to the accuracy of the
model, so as to eliminate complex redundant subtrees
and enhance convergence speed. Finally, it obtains
simple, reliable and exact linear in parameters
nonlinear model via GP evolution. Simulations validate
that the proposed method can generate more robust and
interpretable models, which is obvious and easy for
realization in real applications. For the proposed
algorithm, the whole modeling process is fully
automatic, which is a rather promising method for
complex process intelligent modeling.",
-
bibdate = "2011-02-22",
-
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca19.html#WeiLF10",
-
affiliation = "Air Force Engineering University Engineering Institute
Department of Aircraft and Power Engineering Shaanxi,
Xi'an 710038 China",
- }
Genetic Programming entries for
Xunkai Wei
Yinghong Li
Yue Feng
Citations