Optimising a neural tree classifier using a genetic algorithm

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

Optimising a neural tree classifier using a genetic algorithm

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

58 Downloads (Pure)

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
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

Access to Document

900911Accepted author manuscript, 67 KB

Cite this

@inproceedings{bb8e8f66dc4b4376a88ece02d9689f7a,

title = "Optimising a neural tree classifier using a genetic algorithm",

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",

author = "W. Pensuwon and R.G. Adams and N. Davey",

year = "2000",

language = "English",

isbn = "0780364007",

volume = "2",

pages = "848--851",

booktitle = "Proceedings of the 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES'2000)",

}

TY - GEN

T1 - Optimising a neural tree classifier using a genetic algorithm

AU - Pensuwon, W.

AU - Adams, R.G.

AU - Davey, N.

PY - 2000

Y1 - 2000

N2 - 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

AB - 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

M3 - Conference contribution

SN - 0780364007

VL - 2

SP - 848

EP - 851

BT - Proceedings of the 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES'2000)

ER -

Optimising a neural tree classifier using a genetic algorithm

Abstract

Access to Document

Fingerprint

Cite this