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Self-Adaptive Genetic Algorithm for Clustering

Juha Kivijärvi, Pasi Fränti, Olli Nevalainen, Self-Adaptive Genetic Algorithm for Clustering. Journal of Heuristics 9(2), 113–129, 2003.

Abstract:

Clustering is a hard combinatorial problem which has many applications
in science and practice. Genetic algorithms (GAs) have turned out to be
very effective in solving the clustering problem. However, GAs have
many parameters, the optimal selection of which depends on the problem
instance. We introduce a new self-adaptive GA that finds the parameter
setup on-line during the execution of the algorithm. In this way, the
algorithm is able to find the most suitable combination of the
available components. The method is robust and achieves results
comparable to or better than a carefully fine-tuned non-adaptive GA.

BibTeX entry:

@ARTICLE{jKiFrNe03a,
  title = {Self-Adaptive Genetic Algorithm for Clustering},
  author = {Kivijärvi, Juha and Fränti, Pasi and Nevalainen, Olli},
  journal = {Journal of Heuristics},
  volume = {9},
  number = {2},
  pages = {113–129},
  year = {2003},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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