Please use this identifier to cite or link to this item:
Title: A comparative study of immune system based genetic algorithms in dynamic environments
Authors: Yang, S
Keywords: Immune system based genetic algorithms;Transformation;Memory;Random immigrants;Dynamic environments
Issue Date: 2006
Publisher: ACM
Citation: In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), Seattle, Washington, USA: 1377 - 1384, 08-12 Jul 2006
Abstract: Diversity and memory are two major mechanisms used in biology to keep the adaptability of organisms in the ever-changing environment in nature. These mechanisms can be integrated into genetic algorithms to enhance their performance for problem optimization in dynamic environments. This paper investigates several GAs inspired by the ideas of biological immune system and transformation schemes for dynamic optimization problems. An aligned transformation operator is proposed and combined to the immune system based genetic algorithm to deal with dynamic environments. Using a series of systematically constructed dynamic test problems, experiments are carried out to compare several immune system based genetic algorithms, including the proposed one, and two standard genetic algorithms enhanced with memory and random immigrants respectively. The experimental results validate the efficiency of the proposed aligned transformation and corresponding immune system based genetic algorithm in dynamic environments.
Description: Copyright @ 2006 ACM
ISBN: 1-59593-186-4
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf326.02 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.