Retinal Vessel Segmentation Combined Two-Dimensional Entropy Method and Double Populations Genetic Algorithm

2017 ◽  
Vol 31 (05) ◽  
pp. 1754008 ◽  
Author(s):  
Li Zhang ◽  
Kai-Teng Wu ◽  
Tao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Retinal Vessel ◽  
Entropy Method ◽  
Retinal Vessels ◽  
Two Dimensional ◽  
Noise Resistance ◽  
Threshold Method ◽  
Retinal Vessel Segmentation ◽  
Finite Sampling ◽  
Combined Algorithm

In order to overcome the disadvantages such as finite sampling space and local optimal of genetic algorithm, the main objective of this paper is to combine double populations genetic algorithm and two-dimensional maximum entropy threshold method for retinal vessels segmentation. The proposed method is able to segment retinal vessels image accurately and keep connectivity and smoothness of vessels through the numerical experiments. Numerical results show that the combined algorithm has faster convergence speed, higher calculation accuracy, stronger noise resistance and better performance in reserving pathological information compared with other algorithms.

scholarly journals Double Genes Improved Genetic Algorithm for Solving Two-Dimensional Rectangular Layout Problem

2021 ◽  
Vol 2138 (1) ◽  
pp. 012007
Author(s):  
Min Cui ◽  
Kun Yang ◽  
Xiangming Deng ◽  
Shuqing Lyu ◽  
Miaomiao Feng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Population Diversity ◽  
Utilization Rate ◽  
Two Dimensional ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Double Mutation ◽  
Mutation Operators ◽  
Random Crossover

Abstract Two-dimensional rectangular layout is according to the number of rectangular pieces and the size of the area of the rectangular pieces into the plate. Depending on the iteration of population in genetic algorithm, better utilization rate of plate is obtained. However, due to the characteristics of vertical and horizontal rows of rectangular pieces, relying on the sequence of rectangular pieces alone as the gene cannot guarantee the genetic diversity of the population, and leads to premature algorithm. In view of the special characters of rectangular layout, Double Genes improved genetic algorithm is proposed according to the order of rectangular layout and its own placement characteristics. In order to improve population diversity, Angle genes were added on the basis of rectangular sequencing genes. In view of the particularity of double genes, double random crossover operators and double mutation operators are proposed to improve the population diversity and randomness of genetic algorithm. Experimental results show the effectiveness of the improved algorithm.

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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