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In this paper we present and study a clustering technique based on genetic algorithms Clustering Genetic Algorithm. Performance of the algorithm is demonstrated on experiments. We have shown that it outperforms the k-means algorithm on some tasks. In addition, it is capable of optimising the number of clusters for tasks with well formed and separated(More)
A multiagent system targeted toward the area of computational intelligence modeling is presented. The purpose of the system is to allow both experiments and high-performance distributed computations employing hybrid computational models. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence(More)
Processing of huge amount of categorical data is a subject of great interest in present. Many new techniques for analysis of such data were developed. In this paper we compare several selected clustering approaches, using both available software packages and our implementation of neural network clustering techniques and genetic algorithms.
In this work we study and develop learning algorithms for networks based on regulariza-tion theory. In particular, we focus on learning possibilities for a family of regularization networks and radial basis function networks (RBF networks). The framework above the basic algorithm derived from theory is designed. It includes an estimation of a(More)
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