Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution

Autor:	Miha Kovačič, Leo Gusel, Miran Brezocnik
Rok vydání:	2005
Předmět:	Mathematical optimization Variables Materials science Mechanical Engineering media_common.quotation_subject Genetic programming Industrial and Manufacturing Engineering Stress (mechanics) Mechanics of Materials Position (vector) Genetic algorithm Applied mathematics General Materials Science Node (circuits) Radial stress media_common Test data
Zdroj:	Materials and Manufacturing Processes. 20:497-508
ISSN:	1532-2475 1042-6914
DOI:	10.1081/amp-200053541
Popis:	This article compares genetic algorithm (GA) and genetic programming (GP) for system modeling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analyzed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d696a52289f2012d9c9b0dd78d91cf6f https://doi.org/10.1081/amp-200053541 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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