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Title: An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems
Authors: Liu, L
Wang, D
Yang, S
Keywords: Dynamic environments
Genetic algorithms
Traveling salesman
Publication Date: 2009
Publisher: Springer Verlag
Citation: EvoWorkshops 2009: Applications of Evolutionary Computing, Lecture Notes in Computer Science 5484: 725 - 734, 2009
Abstract: In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.
Description: Copyright @ Springer-Verlag Berlin Heidelberg 2009.
Sponsorship: This work was supported by the Key Program of National Natural Science Foundation (NNSF) of China under Grant No. 70431003 and Grant No. 70671020, the Science Fund for Creative Research Group of NNSF of China under GrantNo. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1.
ISSN: 0302-9743
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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