{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T14:47:12Z","timestamp":1648738032215},"reference-count":9,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2002,10]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0129065702001242","type":"journal-article","created":{"date-parts":[[2002,11,5]],"date-time":"2002-11-05T09:55:24Z","timestamp":1036490124000},"page":"399-410","source":"Crossref","is-referenced-by-count":0,"title":["GENETIC PROGRAMMING OF POLYNOMIAL HARMONIC NETWORKS USING THE DISCRETE FOURIER TRANSFORM"],"prefix":"10.1142","volume":"12","author":[{"given":"NIKOLAY Y.","family":"NIKOLAEV","sequence":"first","affiliation":[{"name":"Dept. of Math. and Computing Sciences,  Goldsmiths College, University of London, New Cross,  London SE14 6NW, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HITOSHI","family":"IBA","sequence":"additional","affiliation":[{"name":"Dept. of Inf. and Comm. Engineering, School of  Engineering, The University of Tokyo, 7-3-1 Hongo,  Bunkyo-ku, Tokyo 113-8656, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"p_1","first-page":"5","author":"Gallant A. R.","year":"1992","journal-title":"H. White, Artificial Neural Networks: Approximation and Learning Theory (Basil Blackwell, Oxford, UK)"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1109\/72.774207"},{"key":"p_5","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065700000119"},{"key":"p_7","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1971.4308320"},{"key":"p_13","doi-asserted-by":"publisher","DOI":"10.1109\/4235.942530"},{"key":"p_18","doi-asserted-by":"publisher","DOI":"10.1007\/BF02506337"},{"key":"p_19","doi-asserted-by":"publisher","DOI":"10.1126\/science.267326"},{"key":"p_20","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1997.5.2.213"},{"key":"p_24","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065701000898"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065702001242","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T02:09:27Z","timestamp":1565143767000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065702001242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,10]]},"references-count":9,"journal-issue":{"issue":"05","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2002,10]]}},"alternative-id":["10.1142\/S0129065702001242"],"URL":"https:\/\/doi.org\/10.1142\/s0129065702001242","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,10]]}}}