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Optimizing Least-Cost Steiner Tree in Graphs via an Encoding-Free Genetic Algorithm

  • Qing LiuEmail author
  • Rongjun Tang
  • Jingyan Kang
  • Junliang Yao
  • Wenqing Wang
  • Yali Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10385)

Abstract

Most bio-inspired algorithms for solving the Steiner tree problem (STP) require the procedures of encoding and decoding. The frequent operations on both encoding and decoding inevitably result in serious time consumption and extra memory overhead, and then reduced the algorithms’ practicability. If a bio-inspired algorithm is encoding-free, its practicability will be improved. Being motivated by this thinking, this article presents an encoding-free genetic algorithm in solving the STP. To verify our proposed algorithm’s validity and investigate its performance, detailed simulations were carried out. Some insights in this article may also have significance for reference when solving the other problems related to the topological optimization.

Keywords

Steiner tree problem Genetic algorithm Encoding-free Tree-based genotype 

Notes

Acknowledgements

This research is supported in part by National Science Foundation of China (No. 61502385, No. 61401354, No. 61503299), Key Basic Research Fund of Shaanxi Province (2016JQ6015), and Scientific Research Program Funded by Shaanxi Provincial Education Department (No. 16JK1554).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qing Liu
    • 1
    • 2
    Email author
  • Rongjun Tang
    • 1
  • Jingyan Kang
    • 1
  • Junliang Yao
    • 1
    • 2
  • Wenqing Wang
    • 1
    • 2
  • Yali Wu
    • 1
    • 2
  1. 1.Faculty of Automation and Information EngineeringXi’an University of TechnologyXi’anChina
  2. 2.Shaanxi Key Laboratory of Complex System Control and Intelligent Information ProcessingXi’anChina

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