Referenced Items are not available for this document.
Guided mutation uses the idea of estimation of distribution algorithms to improve conventional mutation operators. It combines global statistical information and the location information of good individual solutions for generating new trial solutions. This paper suggests using guided mutation in iterative local search. An experimental comparison between a conventional iterated local search (CILS) and an iterated local search with guided mutation has been conducted on four classes of the test instances of the quadratic assignment problem.
Published in:
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Date of Conference: 0-0 0
- Page(s):
- 924 - 929
- Print ISBN:
- 0-7803-9487-9
- INSPEC Accession Number:
- 9723533
- Conference Location :
- Vancouver, BC
- Digital Object Identifier :
- 10.1109/CEC.2006.1688410
- Product Type:
- Conference Publications
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 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
