An adaptive genetic algorithm for the minimal switching graph problem

Tang, Maolin (2005) An adaptive genetic algorithm for the minimal switching graph problem. In Lecture Notes in Computer Science, Springer, Lausanne, Switzerland, pp. 224-233.

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Minimal Switching Graph (MSG) is a graph-theoretic representation of the constrained via minimization problem — a combinatorial optimization problem in integrated circuit design automation. From a computational point of view, the problem is NP-complete. Hence, a genetic algorithm (GA) was proposed to tackle the problem, and the experiments showed that the GA was efficient for solving large-scale via minimization problems. However, it is observed that the GA is sensitive to the permutation of the genes in the encoding scheme. For an MSG problem, if different permutations of the genes are used the performances of the GA are quite different. In this paper, we present a new GA for MSG problem. Different from the original GA, this new GA has a self-adaptive encoding mechanism that can adapt the permutation of the genes in the encoding scheme to the underlying MSG problem. Experimental results show that this adaptive GA outperforms the original GA.

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ID Code: 8667
Item Type: Conference Paper
Refereed: Yes
Additional Information: For more information, please refer to the conference's website (see hypertext link) or contact the author.
Author contact details:
Keywords: genetic algorithm, adaptation, graph theory, optimisation
DOI: 10.1007/b107115
ISBN: 9783540253372
ISSN: 1611-3349
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Microelectronics and Integrated Circuits (090604)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2005 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. Lecture Notes in Computer Science
Deposited On: 16 Jul 2007 00:00
Last Modified: 29 Feb 2012 13:14

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