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Clustering by a parallel self-adaptive genetic algorithm

Juha Kivijärvi, Joonas Lehtinen, Olli Nevalainen, Clustering by a parallel self-adaptive genetic algorithm. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), 66-70, 2002.

Abstract:

We describe a new parallel self-adaptive GA for solving the data clustering problem. The algorithm utilizes island parallelization model implemented
using genebank model, in which GA processes communicate with each other only through the genebank process. The genebank process maintains
a population of best solutions. This model allows one to easily implement
different migration topologies. Experiments show that significant speedup can be reached by parallelization. The effect of migration parameters is also studied and the development of diversity is examined by several measures, some of which are new.

BibTeX entry:

@INPROCEEDINGS{pKiLeNe02a,
  title = {Clustering by a parallel self-adaptive genetic algorithm},
  booktitle = {Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02)},
  author = {Kivijärvi, Juha and Lehtinen, Joonas and Nevalainen, Olli},
  pages = {66-70},
  year = {2002},
}

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

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