By Topic

Convergence and calculation efficiency analysis of abstract model of nonlinear genetic algorithm based on function group

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
$33 $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)
Cui Zhi-Hua ; Div. of Syst. & Simulation & Comput. Application, Taiyuan Heavy Machinery Inst., China ; Zeng Jian-chao ; Xu Yu-Bin

Through mechanism analysis of genetic algorithm (GAs), every genetic operator of SGA and their combination action can be considered as linear transformations to the corresponding individuals. By modifying the traditional genetic operators, the nonlinear genetic algorithm (NGA) is introduced. In this paper, abstract model of NGA based on function group is discussed in which every function is selected with some probability, and if the function group is correctly selected, then the algorithm can be convergent to global optimal within every given generation number. With this technique the premature convergence and calculation efficiency may be solved.

Published in:

Systems, Man and Cybernetics, 2003. IEEE International Conference on  (Volume:5 )

Date of Conference:

5-8 Oct. 2003