Optimising a neural tree classifier using a genetic algorithm

W. Pensuwon, R.G. Adams, N. Davey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 languageEnglish
Title of host publicationProceedings of the 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES'2000)
Publication statusPublished - 2000


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