Application of Multiobjective Genetic Programming to the Design of Robot Failure Recognition Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Zhang:2009:ieeetASE,
-
author = "Yang Zhang and Peter I. Rockett",
-
title = "Application of Multiobjective Genetic Programming to
the Design of Robot Failure Recognition Systems",
-
journal = "IEEE Transactions on Automation Science and
Engineering",
-
year = "2009",
-
month = apr,
-
volume = "6",
-
number = "2",
-
pages = "372--376",
-
keywords = "genetic algorithms, genetic programming, classifiers,
data-driven machine learning method, domain knowledge,
domain-dependent feature extraction, multiobjective
genetic programming, robot failure recognition systems,
control engineering computing, feature extraction,
learning (artificial intelligence), telerobotics",
-
abstract = "We present an evolutionary approach using
multiobjective genetic programming (MOGP) to derive
optimal feature extraction preprocessing stages for
robot failure detection. This data-driven machine
learning method is compared both with conventional
(nonevolutionary) classifiers and a set of
domain-dependent feature extraction methods. We
conclude MOGP is an effective and practical design
method for failure recognition systems with enhanced
recognition accuracy over conventional classifiers,
independent of domain knowledge.",
-
DOI = "doi:10.1109/TASE.2008.2004414",
-
ISSN = "1545-5955",
-
notes = "Also known as \cite{4667633}",
- }
Genetic Programming entries for
Yang Zhang
Peter I Rockett
Citations