- author = "Nikolay Y. Nikolaev and Hitoshi Iba",
- title = "Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform",
- journal = "International Journal of Neural Systems",
- year = "2002",
- volume = "12",
- number = "5",
- pages = "399--410",
- keywords = "genetic algorithms, genetic programming, Polynomial neural networks, polynomial harmonic networks, time-series prediction",
-
DOI = "
10.1142/S0129065702001242",
- 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.",
-
notes = "IJNS
Dept. of Math. and Computing Sciences, Goldsmiths College, University of London, New Cross, London SE14 6NW, United Kingdom
Dept. of Inf. and Comm. Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan",
