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Self-Adaptive Genetic Algorithm for Clustering

Juha Kivijärvi, Pasi Fränti, Olli Nevalainen, Self-Adaptive Genetic Algorithm for Clustering. TUCS Technical Reports 308, Turku Centre for Computer Science, 1999.

Abstract:

Clustering is a hard combinatorial problem which has many applications in sci- ence and practice. Genetic algorithms (GAs) have turned out to be very effective in solving the clustering problem. However, GAs have many parameters, the op- timal 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 com- bination of the available components. The method is robust and achieves results comparable to or better than a carefully fine-tuned non-adaptive GA.

<p>For full paper contact juhkivij@utu.fi.

BibTeX entry:

@TECHREPORT{tKiFrNe99a,
  title = {Self-Adaptive Genetic Algorithm for Clustering},
  author = {Kivijärvi, Juha and Fränti, Pasi and Nevalainen, Olli},
  number = {308},
  series = {TUCS Technical Reports},
  publisher = {Turku Centre for Computer Science},
  year = {1999},
  keywords = {clustering, evolutionary computing, genetic algorithms, self-adaptation},
  ISBN = {952-12-0546-6},
}

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