| Genetic algorithm-based evaluation of spatial straightness error |
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| 作者姓名： | 崔长彩 车仁生 黄庆成 叶东 陈刚 |
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| 作者单位： | Dept. of Automation Measurement and Control，Harbin Institute of Technology，Dept. of Automation Measurement and Control，Harbin Institute of Technology，Dept. of Automation Measurement and Control，Harbin Institute of Technology，Dept. of Automation Measurement and Control，Harbin Institute of Technology，Dept. of Automation Measurement and Control，Harbin Institute of Technology Harbin 150001，China，Harbin 150001，China，Harbin 150001，China，Harbin 150001，China，Harbin 150001，China |
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| 摘 要： | ~~Genetic algorithm-based evaluation of spatial straightness error@崔长彩$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,China
@车仁生$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,China
@黄庆成$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,China
@叶东$Dept. of Automation Measurement and Control,Harbin Institute of Technology!Harbin 150001,Chi…
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Genetic algorithm-based evaluation of spatial straightness error |
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| CUI Chang-cai,CHE Ren-sheng,HUANG Qing-cheng,YE Dong,CHEN Gang.Genetic algorithm-based evaluation of spatial straightness error[J].Journal of Harbin Institute of Technology,2003,10(4). |
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| Authors: | CUI Chang-cai CHE Ren-sheng HUANG Qing-cheng YE Dong CHEN Gang |
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| Affiliation: | Dept. of Automation Measurement and Control, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: | A genetic algorithm(GA)-based approach is proposed to evaluate the straightness error of spatial lines. According to the mathematical definition of spatial straightness, a verification model is established for straightness error, and the fitness function of GA is then given and the implementation techniques of the proposed algorithm is discussed in detail. The implementation techniques include real number encoding,adaptive variable range choosing, roulette wheel and elitist combination selection strategies,heuristic crossover and single point mutation schemes etc.An applicatin example is quoted to validate the proposed algorithm. The computation result shows that the GA-based approach is a superior nonlinear parallel optimization method. The performance of the evolution population can be improved through genetic operations such as reproduction,crossover and mutation until the optimum goal of the minimum zone solution is obtained. The quality of the solution is better and the efficiency of computation is higher than other methods. |
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| Keywords: | straightness genetic algorithm evaluation |
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