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

A simultaneous parameter adaptation scheme for genetic algorithms with application to phased array synthesis

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)
Boeringer, D.W. ; Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA ; Werner, D.H. ; Machuga, D.W.

Genetic algorithms are commonly used to solve many optimization and synthesis problems. An important issue facing the user is the selection of genetic algorithm parameters, such as mutation rate, mutation range, and number of crossovers. This paper demonstrates a real-valued genetic algorithm that simultaneously adapts several such parameters during the optimization process. This adaptive algorithm is shown to outperform its static counterparts when used to synthesize the phased array weights to satisfy specified far-field sidelobe constraints, and can perform amplitude-only, phase-only, and complex weight synthesis. When compared to conventional static parameter implementations, computation time is saved in two ways: 1) The algorithm converges faster and 2) the need to tune parameters by hand (generally done by repeatedly running the code with different parameter choices) is greatly reduced. By requiring less iteration to solve a given problem, this approach may benefit electromagnetic optimization problems with expensive cost functions, since genetic algorithms generally require many function evaluations to converge. The adaptive process also provides insight into the qualitative importance of parameters, and dynamically adjusting the mutation range is found to be especially beneficial.

Published in:

Antennas and Propagation, IEEE Transactions on  (Volume:53 ,  Issue: 1 )

Date of Publication:

Jan. 2005

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.