GPSO: A Framework for Optimization of Genetic Programming Classifier Expressions for Binary Classification Using Particle Swarm Optimization
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Jabeen:2012:ijicic,
-
author = "Hajira Jabeen and Abdul Rauf Baig",
-
title = "GPSO: A Framework for Optimization of Genetic
Programming Classifier Expressions for Binary
Classification Using Particle Swarm Optimization",
-
journal = "International journal of innovative computing,
information and control",
-
year = "2012",
-
volume = "8",
-
number = "1 A",
-
pages = "233--242",
-
month = jan,
-
keywords = "genetic algorithms, genetic programming,
classification, particle swarm optimisation,
optimisation, expressions",
-
ISSN = "1349-418X",
-
publisher = "ICIC international",
-
URL = "http://www.ijicic.org/ijicic-10-06097.pdf",
-
size = "10 pages",
-
abstract = "Genetic Programming (GP) is an emerging classification
tool known for its flexibility, robustness and
lucidity. However, GP suffers from a few limitations
like long training time, bloat and lack of convergence.
In this paper, we have proposed a hybrid technique that
overcomes these drawbacks by improving the performance
of GP evolved classifiers using Particle Swarm
Optimisation (PSO). This hybrid classification
technique is a two-step process. In the first phase, we
have used GP for evolution of arithmetic classifier
expressions (ACE). In the second phase, we add weights
to these expressions and optimise them using PSO. We
have compared the performance of proposed frame- work
(GPSO) with the GP classification technique over twelve
benchmark data sets. The results conclude that the
proposed optimisation strategy outperforms GP with
respect to classification accuracy and less
computation.",
- }
Genetic Programming entries for
Hajira Jabeen
Abdul Rauf Baig
Citations