Using of genetic programming in engineering

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{babic:2014:etv,

• author = "Matej Babic and Peter Kokol and Igor Belic and Peter Panjan and Miha Kovacic and Joze Balic",
• title = "Using of genetic programming in engineering",
• journal = "Elektrotehniski vestnik",
• year = "2014",
• volume = "81",
• number = "3",
• pages = "143--147",
• month = jul,
• keywords = "genetic algorithms, genetic programming, engineering, complex geometry structure",
• ISSN = "0013-5852",
• URL = "http://ev.fe.uni-lj.si/3-2014/Babic.pdf",
• size = "5 pages",
• abstract = "Intelligent systems are process coupled with robotics in industrial usually settings, though they may be used as diagnostic systems connected only to passive sensors. In this paper we use a new method which combines an intelligent genetic algorithm and multiple regression to predict the hardness of hardened specimens. The hardness of a material is an important mechanical property affecting mechanical properties of materials. The Microstructures of the hardened specimens are very complex and cannot be described them with the classical Euclidian geometry. Thus, we use a new method, i.e. fractal geometry. By using the method intelligent-system, genetic programming and multiple regression, improved production the process laser-hardening increases because of the decreased time of the process and, the improved increased topographical property of the used materials. The genetic-programming modelling results show a good agreement with the measured hardness of the hardened specimens.",
• abstract_si = "Inteligentni sistemi naj bi se po navadi povezali skupaj z robotiko v nastavitvah industrijskih procesov, ceprav so lahko sistemi za diagnostiko povezani samo za pasivne senzorje. Vtem clanku bomo uporabili metodo, ki zdruzuje inteligentne genetske algoritme in multiplo regresijo za napoved trdote kaljenih vzorcev. Trdota materiala je pomembna mehanska lastnost, ki vpliva na mehanske lastnosti materialov. Mikrostrukture kaljenih vzorcev so zelo kompleksne in jih ne moremo opisati s klasicno evklidsko geometrijo. Zato smo uporabili novo metodo, fraktalno geometrijo. Z metodo inteligentnega sistema, genetskim programiranjem in multiplo regresijo smo povecali proizvodnjo pri laserskem kaljenju, saj smo skrajšali cas procesa in povecali topografsko lastnost materiala. Rezultati modeliranja genetskega programiranja se dobro ujemajo z izmerjenimi vrednostmi trdote kaljenih vzorcev.",
• notes = "http://ev.fe.uni-lj.si/online.html Journal of Electrical Engineering and Computer Science",

}

Genetic Programming entries for Matej Babic Peter Kokol Igor Belic Peter Panjan Miha Kovacic Joze Balic

Citations