Error Evaluation of Planar Curve Profile Based on an Improved Genetic Algorithm

2017 ◽  
Author(s):  
Kuan Lu ◽  
Yuan Yu ◽  
Kaiyuan Peng ◽  
Liyan Yang
Keyword(s):  
Genetic Algorithm ◽  
Error Evaluation ◽  
Improved Genetic Algorithm ◽  
Planar Curve ◽  
Curve Profile

Evaluating freeform curve profile error based on improved genetic algorithm and quasi random sequence

2012 ◽  
Vol 20 (4) ◽  
pp. 835-842
Author(s):  
温秀兰 WEN Xiu-lan ◽  
赵艺兵 ZHAO Yi-bing ◽  
王东霞 WANG Dong-xia ◽  
朱晓春 ZHU Xiao-chun ◽  
曹未丰 CAO Wei-feng
Keyword(s):  
Genetic Algorithm ◽  
Random Sequence ◽  
Improved Genetic Algorithm ◽  
Profile Error ◽  
Curve Profile

The Research of the Genetic Algorithm Combined with Chromosome Fitness to Optimize the Flatness Error Evaluation

2013 ◽  
Vol 278-280 ◽  
pp. 1342-1348
Author(s):  
Ying Di Guo ◽  
Wei Cong Wu ◽  
Mi Jia Jiang ◽  
Bo Nan Li ◽  
Bing Bing Fang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Industrial Production ◽  
Measurement Accuracy ◽  
Error Evaluation ◽  
Improved Genetic Algorithm ◽  
Simulation Experiments ◽  
Flatness Error ◽  
Flatness Measurement ◽  
Better Than ◽  
The Ideal

This paper suggests an improved genetic algorithm to seek the minimum range value in the ideal-plane flatness measurement. This algorithm increases measurement accuracy by using dynamic cross factor, mutation factor and a new concept called chromosome fitness. It was proved in simulation experiments that its accuracy is better than other flatness error evaluating algorithms like the minimal territory evaluating algorithm and the computational geometry algorithm etc. So it can be used for measuring industrial production components error and verifying assumed models in reverse engineering etc.

Improved genetic algorithm for maximum clique problem

2009 ◽  
Vol 28 (12) ◽  
pp. 3072-3073
Author(s):  
Dong-hui WU ◽  
Liang MA
Keyword(s):  
Genetic Algorithm ◽  
Maximum Clique ◽  
Maximum Clique Problem ◽  
Improved Genetic Algorithm ◽  
Clique Problem

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

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