By Topic

Fuzzy-neural tuned genetic algorithm applied to large-space constraint satisfaction

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)
Tiehua Zhang ; Sch. of Eng. Sci., Simon Fraser Univ., Burnaby, BC, Canada ; Gruver, W.A.

The paper treats a fuzzy-neural tuned genetic algorithm for solving a constraint satisfaction problem for an industrial application. It describes the design of a reflecting lamp composed of five consecutive straight mirror segments that satisfy both illumination efficiency and uniformity properties. An analytically established neural network dynamically controls the genetic algorithm mutation rate and the convergence criteria. The neural network implements a six-rule fuzzy system that gains its knowledge from a human operator and works in a similar way to monitor the convergence process. Using numerical experiments the lamp configurations are determined. The proposed method can also be applied to other optimization design tasks

Published in:

Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on  (Volume:4 )

Date of Conference:

11-14 Oct 1998