Cart (Loading....) | Create Account
Close category search window
 

A Pseudo-Parallelism Genetic Algorithm Framework to Optimization of Neural Networks

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

3 Author(s)
Shu-hai Zhao ; Sch. of Manage., Univ. of JiNan, Jinan ; Li Shao ; Jin-zhu Ma

This paper present a new approach, combined pseudo- parallelism evolution technique based on sub-population competition with parent mutation mechanism, for automatic topology optimization of multilayer feedforward neural networks. It allows that two networks with different number of individuals can be crossed to a new valid "child" network. The calculation result of an example shows that PPGA is able to get the real-time information of population diversity during the process of evolution and has some improvements in both global converging velocity and searching precision.

Published in:

Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on

Date of Conference:

23-24 May 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.