An Adaptive Genetic Algorithm for the Minimal Switching Graph Problem

  • Maolin Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3448)


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.


Genetic Algorithm Encode Scheme Crossover Operator Binary String Adaptive Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maolin Tang
    • 1
  1. 1.School of Software Engineering and Data CommunicationsQueensland University of TechnologyBrisbaneAustralia

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