On the Analysis of the Immune-Inspired B-Cell Algorithm for the Vertex Cover Problem

  • Thomas Jansen
  • Pietro S. Oliveto
  • Christine Zarges
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6825)


The runtime of the immune inspired B-Cell Algorithm (BCA) for the NP-hard vertex cover problem is analysed. It is the first theoretical analysis of a nature-inspired heuristic as used in practical applications for a realistic problem. Since the performance of BCA in combinatorial optimisation strongly depends on the representation an encoding heuristic is used. The BCA outperforms mutation-based evolutionary algorithms (EAs) on instance classes that are known to be hard for randomised search heuristics (RSHs). With respect to average runtime, it even outperforms a crossover-based EA on an instance class previously used to show good performance of crossover. These results are achieved by the BCA without needing a population. This shows contiguous somatic hypermutation as an alternative to crossover without having to control population size and diversity. However, it is also proved that populations are necessary for the BCA to avoid arbitrarily bad worst case approximation ratios.


Approximation Ratio Vertex Cover Markov Chain Model Instance Class Vertex Cover Problem 
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 2011

Authors and Affiliations

  • Thomas Jansen
    • 1
  • Pietro S. Oliveto
    • 2
  • Christine Zarges
    • 3
  1. 1.Department of Computer ScienceUniversity College CorkCorkIreland
  2. 2.School of Computer ScienceUniversity of BirminghamBirminghamUK
  3. 3.Fakultät für InformatikTU DortmundDortmundGermany

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