University of Hertfordshire

Optimising a neural tree classifier using a genetic algorithm

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


  • 900911

    Accepted author manuscript, 67 KB, PDF document

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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


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

ID: 422484