Abstract
This paper documents experiments performed using a GA to optimise the parameters of a dynamic neural tree model. Two fitness functions were created from two selected clustering measures, and a population of genotypes, specifying parameters of the model were evolved. This process mirrors genomic evolution and ontogeny. It is shown that the evolved parameter values improved performance
Original language | English |
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Title of host publication | Proceedings of the 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES'2000) |
Pages | 848-851 |
Volume | 2 |
Publication status | Published - 2000 |