Genetic algorithm-based evaluation of spatial straightness error

被引:0
|
作者
崔长彩
车仁生
黄庆成
叶东
陈刚
机构
[1] China
[2] Dept. of Automation Measurement and Control
[3] Harbin Institute of Technology
[4] Harbin Institute of Technology Harbin 150001
关键词
straightness; genetic algorithm; evaluation;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:418 / 421
页数:4
相关论文
共 50 条
  • [41] Genetic algorithm-based detection of the layout of color yarns
    Pan, Ruru
    Gao, Weidong
    Liu, Jihong
    Wang, Hongbo
    JOURNAL OF THE TEXTILE INSTITUTE, 2011, 102 (02) : 172 - 179
  • [42] A genetic algorithm-based dendritic cell algorithm for input signal generation
    Zhang, Dan
    Zhang, Yu
    Liang, Yiwen
    APPLIED INTELLIGENCE, 2023, 53 (22) : 27571 - 27588
  • [43] A genetic algorithm-based satellite image retrieval model
    Huang, Yo-Ping
    Chang, Tsun-Wei
    Liu, Dankai
    EISTA '06: 4TH INT CONF ON EDUCATION AND INFORMATION SYSTEMS: TECHNOLOGIES AND APPLICAT/SOIC'06: 2ND INT CONF ON SOCIAL AND ORGANIZATIONAL INFORMATICS AND CYBERNETICS, VOL II, 2006, : 123 - +
  • [44] Genetic Algorithm-based Crowdsensing for Cognitive Radio Networks
    Mossad, Omar S.
    ElNainay, Mustafa
    2018 14TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB 2018), 2018,
  • [45] Population Adaptation for Genetic Algorithm-based Cognitive Radios
    Timothy R. Newman
    Rakesh Rajbanshi
    Alexander M. Wyglinski
    Joseph B. Evans
    Gary J. Minden
    Mobile Networks and Applications, 2008, 13 : 442 - 451
  • [46] A genetic algorithm-based method for feature subset selection
    Feng Tan
    Xuezheng Fu
    Yanqing Zhang
    Anu G. Bourgeois
    Soft Computing, 2008, 12 : 111 - 120
  • [47] A genetic algorithm-based dendritic cell algorithm for input signal generation
    Dan Zhang
    Yu Zhang
    Yiwen Liang
    Applied Intelligence, 2023, 53 : 27571 - 27588
  • [48] Research on Genetic Algorithm-based Solution Method for Variable Cycle Engine Model
    Wu Zhengjia
    Meng Ronghua
    Li Ji
    MECHANICAL COMPONENTS AND CONTROL ENGINEERING III, 2014, 668-669 : 633 - +
  • [49] Genetic Algorithm-based Reliability of Computer Communication Network
    He, Yifeng
    Zhang, Rui
    Ye, Nan
    IETE JOURNAL OF RESEARCH, 2022,
  • [50] A Genetic Algorithm-based Construction of Fractional Repetition Codes
    Deng, Zhihang
    Zhu, Bing
    Du, Xianzhi
    Shum, Kenneth W.
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 4244 - 4249