Memetic Computing

, Volume 7, Issue 3, pp 181–201 | Cite as

A hybridization of clonal selection algorithm with iterated local search and variable neighborhood search for the feature selection problem

  • Magdalene Marinaki
  • Yannis Marinakis
Regular Research Paper


Nature inspired methods are approaches that are used in various fields and for the solution of a number of problems. This study uses a hybridized version of the clonal selection algorithm, the clonal selection algorithm–iterated local search–variable neighborhood search (CSA–ILS–VNS), for the solution of the feature selection problem (FSP). The clonal selection algorithm is inspired by the clonal selection and affinity maturation process of B cells of the natural immune system once the immune system has detected a pathogen. The proposed clonal selection algorithm is combined with a number of nearest neighbour based classifiers and it is tested using various benchmark data sets from the UCI machine learning repository. The algorithm is compared with variants of the clonal selection algorithm [the classic clonal selection algorithm (CSA), the clonal selection algorithm–iterated local search (CSA–ILS) and the clonal selection algorithm–variable neighborhood search (CSA–VNS)], a particle swarm optimization algorithm, an ant colony optimization algorithm and a genetic algorithm.


Artificial immune systems Clonal selection algorithm  Variable neighborhood search Feature selection problem  Iterated local search 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Production Engineering and ManagementTechnical University of CreteChaniaGreece

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