By Topic

An Improved Genetic Algorithm Based on Fixed Point Theory for Function Optimization

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

4 Author(s)
Jingjun Zhang ; Sci. Res. Office, Hebei Univ. of Eng. Handan, Handan ; Yuzhen Dong ; Ruizhen Gao ; Yanmin Shang

This paper introduces triangulation theory into genetic algorithm and with which, the optimization problem will be translated into a fixed point problem. An improved genetic algorithm is proposed by virtue of the concept of relative coordinates genetic coding, designs corresponding crossover and mutation operator. Through genetic algorithms to overcome the triangulation of the shortcomings of human grade, it can start from any point to find the most advantages. Gradually fine mesh will be introduced the idea of genetic algorithms so that the search area gradually decreased, improving the efficiency of search. Finally, examples demonstrate the effectiveness of this method.

Published in:

Computer Engineering and Technology, 2009. ICCET '09. International Conference on  (Volume:1 )

Date of Conference:

22-24 Jan. 2009