Empirical Analysis of the Spatial Genetic Algorithm on Small-World Networks

  • Yong Min
  • Xiaogang Jin
  • Xianchuang Su
  • Bo Peng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


Genetic algorithm (GA) has been widely used in optimizing and solving various problems since first proposed, and its characters also have been deeply studied. In this paper, we investigate the benefits of genetic algorithm whose population is distributed on small-world networks. In particular, we pay our attention to the complexity of how small-world affects the behavior of spatial GA. Our work shows that, on a complex problem, the behavior of spatial GA on the small-world networks is influenced by at least two different factors: local selection and asymmetric topology. It is more complex than previous results from simple lattice models. Our results could provide lots of potential methods to improve the performance of spatial GA and give some guidance for designing of parallel genetic algorithm. We also present many future problems on the influence of small-world to spatial GA.


Genetic Algorithm Regular Lattice Local Selection Parallel Genetic Algorithm Convergent Speed 
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 2006

Authors and Affiliations

  • Yong Min
    • 1
  • Xiaogang Jin
    • 1
    • 2
  • Xianchuang Su
    • 3
  • Bo Peng
    • 1
  1. 1.AI Institute, College of Computer ScienceZhejiang universityHangzhouChina
  2. 2.Ningbo Institute of TechnologyZhejiang universityNingboChina
  3. 3.College of Software EngineeringZhejiang universityHangzhouChina

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