An improved hybrid strategy combining genetic simulated annealing algorithm and EBP used for image segmentation of test strip

2010 ◽  
Author(s):  
Jiajia Wang ◽  
Xiaozhu Chen ◽  
Jin Tan ◽  
Yaqun Wang
Keyword(s):  
Image Segmentation ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Test Strip ◽  
Hybrid Strategy ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Research on Image Technology with Application of K-Means Based on Genetic Simulated Annealing Algorithm in CT Image Segmentation

2014 ◽  
Vol 1022 ◽  
pp. 269-272
Author(s):  
Ling Li Zhu ◽  
Lan Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Simulated Annealing ◽  
Clustering Analysis ◽  
Simulated Annealing Algorithm ◽  
Local Optimum ◽  
Initial Condition ◽  
Ct Image ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm

Aiming at the characteristic of medical images, this paper presents the improved genetic simulated annealing algorithm with K-means clustering analysis and applies in medical CT image segmentation. This improved genetic simulated annealing algorithm can be used to globally optimize k-means image segmentation functions to solve the locality and the sensitiveness of the initial condition. It can automatically adjust the parameters of genetic algorithm according to the fitness values of individuals and the decentralizing degree of individuals of the population and keep the variety of population for rapidly converging, and it can effectively avoid appearing precocity and plunging into local optimum. The example shows that the method is feasible, and better segmentation results have got to satisfy the request for 3D reconstruction, compared with k-means image segmentation and genetic algorithm based image segmentation.

scholarly journals Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 28 (2) ◽  
pp. 101-109
Keyword(s):  
Simulated Annealing ◽  
Software Testing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Fault Localization ◽  
Good Effect ◽  
Test Case ◽  
Genetic Simulated Annealing Algorithm ◽  
Starting Point ◽  
Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

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