Genetic programming of polynomial harmonic networks using the discrete Fourier transform
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Updated: Sep 28, 2026

Harmonic Nanoparticles for Regenerative Research
Published on: May 1, 2014
Nikolay Y Nikolaev1, Hitoshi Iba
1Dept. of Math. and Computing Sciences, Goldsmiths College, University of London, New Cross, London SE14 6NW United Kingdom. nikolaev@mcs.gold.ac.uk
Abstract:
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.
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