Self-Adaptive Genetic Algorithm for Clustering

Authors:
Juha Kivijärvi;Pasi Fränti;Olli Nevalainen
Affiliations:
Turku Centre for Computer Science (TUCS), Department of Information Technology, University of Turku, FIN-20014 Turku, Finland. juhkivij@utu.fi;Department of Computer Science, University of Joensuu, PB 111, FIN-80101, Joensuu, Finland;Turku Centre for Computer Science (TUCS), Department of Information Technology, University of Turku, FIN-20014 Turku, Finland
Venue:
Journal of Heuristics
Year:
2003

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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.