By Topic

Using Particle Swarm Optimization and Genetic Programming to Evolve Classification Rules

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Liping Yan ; China North Univ., Taiyuan ; Jianchao Zeng

According to analyzing particle swarm optimization (PSO), the structure of genetic programming (GP) and classifier model, PSO algorithm and GP were made to combine to evolve classification rules. Rules were described as binary tree which non-leaf node denoted rule structure and leaf-node was correspond to rule value. Leaf node and non-leaf node employed different evolutionary strategy. First, PSO was applied to evolve leaf node in order to obtain the optimum rule of certain structure, then GP was adopted to optimize rule structure. The best rules were obtained after the twice optimization. Finally, the new method indicated efficiency through experiments on several datasets of UCI

Published in:

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on  (Volume:1 )

Date of Conference:

0-0 0