Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
PADS '96 Proceedings of the tenth workshop on Parallel and distributed simulation
Active Learning with Local Models
Neural Processing Letters
A stochastic self-organizing map for proximity data
Neural Computation
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary algorithms using a neural network like migration scheme
Integrated Computer-Aided Engineering
Median fuzzy c-means for clustering dissimilarity data
Neurocomputing
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Clustering of non-metric data sets often occurs in investigations in medicine and social science. The problem is to find suitable measures which describe similarities and, hence, are applicable to the clustering algorithm. In the present contribution we use evolutionary algorithms EA for clustering. Thereby, the similarity measures determine the respective fitness function for the EA. We consider several fitness functions and derive a new one which allows, additionally, the determination of a useful cluster number. -- For the EA we use a new selection strategy combining the advantages of both the (碌, 驴)- and (碌 + 驴)-strategy and a multiple subpopulation approach with a migration scheme following the collective learning dynamic in self-organizing maps.