Genetic programming of polynomial harmonic networks using the discrete Fourier transform.
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.
Nikolaev
Nikolay Y
NY
Dept. of Math. and Computing Sciences, Goldsmiths College, University of London, New Cross, London SE14 6NW United Kingdom. nikolaev@mcs.gold.ac.uk
Iba
Hitoshi
H
D001185:Artificial Intelligence; D003198:Computer Simulation; D005583:Fourier Analysis; D005823:Genetics; D015233:Models, Statistical; D016571:Neural Networks, Computer