Decision Points in Application of Genetic Algorithm

2015 ◽  
Author(s):  
Biplab Mahapatra ◽  
Sanjay Mohapatra
Keyword(s):  
Genetic Algorithm ◽  
Decision Points

Study on Locating a Master Station of Gas Station

2014 ◽  
Vol 505-506 ◽  
pp. 894-897
Author(s):  
Bing Su ◽  
Na Jia ◽  
Yang Xu ◽  
Xin Feng Cheng
Keyword(s):  
Genetic Algorithm ◽  
Theoretical Basis ◽  
Safety Hazard ◽  
Gas Stations ◽  
Decision Points ◽  
Potential Safety ◽  
Master Station ◽  
Gas Station

Gas station has a potential safety hazard, which would affect lives and property security of residents around it, once an accident occurs, it needs to be rescued immediately. We focus on locating a master station of gas station from the perspective of rescue, add a class of decision points (rescue points) and take rescue costs after the accident into consideration. The model for location is established, and show genetic algorithm for computing it. This provides a theoretical basis for relevant departments planning gas stations location.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

Decision points in osteoporosis

2002 ◽  
Vol 2 (3) ◽  
pp. 135-141
Author(s):  
Susan L. Hendrix ◽  
Richard Derman ◽  
Richard T. Kloos
Keyword(s):  
Decision Points

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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