Research Online

Logo

Goldsmiths - University of London

Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform

Nikolaev, Nikolay and Iba, Hitoshi. 2002. Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform. International Journal of Neural Systems, 12(5), pp. 399-410. ISSN 0129-0657 [Article]

No full text available

Abstract or Description

This paper presents a genetic programming system that evolves polynomial harmonic networks. These are multilayer feed-forward neural networks with polynomial activation functions. The novel hybrids assume that harmonics with non-multiple frequencies may enter as inputs the activation polynomials. The harmonics with non-multiple, irregular frequencies are derived analytically using the discrete Fourier transform. The polynomial harmonic networks have tree-structured topology which makes them especially suitable for evolutionary structural search. Empirical results show that this hybrid genetic programming system outperforms an evolutionary system manipulating polynomials, the traditional Koza-style genetic programming, and the harmonic GMDH network algorithm on processing time series.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1142/S0129065702001242

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
2002Published

Item ID:

16154

Date Deposited:

08 Jan 2016 14:40

Last Modified:

20 Jun 2017 11:17

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/16154

Edit Record Edit Record (login required)