Process synthesis of laser forming by genetic algorithm

2001 ◽  
Author(s):  
Jin Cheng ◽  
Y. Lawrence Yao
Keyword(s):  
Genetic Algorithm ◽  
Process Synthesis ◽  
Laser Forming

Distribution of R- and S-Process Elements in the Galactic Halo

1988 ◽  
Vol 132 ◽  
pp. 501-506
Author(s):  
C. Sneden ◽  
C. A. Pilachowski ◽  
K. K. Gilroy ◽  
J. J. Cowan
Keyword(s):  
Heavy Element ◽  
Galactic Halo ◽  
Low Noise ◽  
Heavy Elements ◽  
Process Synthesis ◽  
Element Abundances ◽  
Halo Stars ◽  
Abundance Patterns ◽  
R Process ◽  
Process Elements

Current observational results for the abundances of the very heavy elements (Z>30) in Population II halo stars are reviewed. New high resolution, low noise spectra of many of these extremely metal-poor stars reveal general consistency in their overall abundance patterns. Below Galactic metallicities of [Fe/H] Ã −2, all of the very heavy elements were manufactured almost exclusively in r-process synthesis events. However, there is considerable star-to-star scatter in the overall level of very heavy element abundances, indicating the influence of local supernovas on element production in the very early, unmixed Galactic halo. The s-process appears to contribute substantially to stellar abundances only in stars more metal-rich than [Fe/H] Ã −2.

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

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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