scholarly journals IGAEM: Improved Genetic Algorithm based Entity Matching

2021 ◽  
Vol 1743 ◽  
pp. 012001
Author(s):  
Y. Aassem ◽  
I. Hafidi ◽  
N. Aboutabit
Author(s):  
Ge Weiqing ◽  
Cui Yanru

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.


2021 ◽  
Vol 183 ◽  
pp. 108041
Author(s):  
Xiuli Chai ◽  
Xiangcheng Zhi ◽  
Zhihua Gan ◽  
Yushu Zhang ◽  
Yiran Chen ◽  
...  

2021 ◽  
Vol 676 (1) ◽  
pp. 012124
Author(s):  
Kai Peng ◽  
Yuefei Zhou ◽  
Yaolai Liu ◽  
Liangliang Ma ◽  
Yi Xie ◽  
...  

Author(s):  
Jiawei Lu ◽  
Wei Zhao ◽  
Haotian Zhu ◽  
Jie Li ◽  
Zhenbo Cheng ◽  
...  

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