Genetic modeling of electrical conductivity of formed material

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@Article{Gusel:2005:MT,

• author = "Leo Gusel and Miran Brezocnik",
• title = "Genetic modeling of electrical conductivity of formed material",
• journal = "Materials and technology",
• year = "2005",
• volume = "39",
• number = "4",
• pages = "107--111",
• email = "mbrezocnik@uni-mb.si",
• keywords = "genetic algorithms, genetic programming, copper alloys, electrical conductivity, cold forming, modelling, genetsko programiranje, modeliranje, hladno preoblikovanje, elektricna prevodnost, bakrove zlitine",
• ISSN = "1580-2949",
• URL = "http://www.imt.si/materiali-tehnologije/",
• URL = "http://ctklj.ctk.uni-lj.si/kovine/izvodi/mit054/gusel.pdf",
• size = "5 pages",
• abstract = "In the paper a genetic programming method for efficient determination of accurate models for the change of electrical conductivity of cold formed alloy CuCrZr was presented. The main characteristic of genetic programming method, which is one of evolutionary methods for modelling, is its non- deterministic way of computing. No assumptions about the form and size of expressions were made in advance, but they were left to the self organisation and intelligence of evolutionary process. Only the best models, gained by genetic programming were presented in the paper. Accuracy of the best models was proved with the testing data set. The comparison between deviation of genetic models results and regression models results concerning the experimental results has showed that genetic models are much more precise and more varied then regression model. The variety of genetic models allows us, concerning the demands, to decide for an optimal genetic model for mathematical description and prediction of change of electrical conductivity in the frame of experimental environment.",
• abstract = "V prispevku smo predstavili metodo genetskega programiranja za uspesno dolocitev natancnih modelov spremembe elektricne prevodnosti hladno preoblikovane zlitine CuCrZr. Glavna znacilnost metode genetskega programiranja, ki spada med evolucijske metode modeliranja, je, da resitev ne iscemo po vnaprej dolocenih poteh ter da socasno obravnavamo mnozico enostavnih objektov. Cedalje natancnejsim resitvam smo se priblizevali postopoma, med postopkom simulirane evolucije. V prispevku smo predstavili le nekatere najuspesnejse oziroma najprimernejse genetske modele. Natancnost genetskih modelov je bila preverjena na mnozici preskusnih tock. Primerjali smo tudi natancnost genetsko dobljenih modelov in modela, dobljenega po deterministicni metodi regresije. Primerjava je pokazala, da se genetski modeli dosti manj odmikajo od eksperimentalnih rezultatov in da so bolj raznoliki. Prav raznolikost nam omogoca, da se, glede na zahteve, odlocimo za optimalen model, s katerim lahko matematicno opisemo ali napovedujemo spremembo elektricne prevodnosti zlitine v okviru eksperimentalnega okolja.",

}

Genetic Programming entries for Leo Gusel Miran Brezocnik

Citations