Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements
碩士 === 國立臺北科技大學 === 光電工程系研究所 === 95 === In this thesis, we study the mutation system of the genetic algorithm (GA) for optimization design of the binary phase diffractive optical elements (DOEs). The GA scheme adopted here was the simple genetic algorithm. We discussed the relationship between param...
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ndltd-TW-095TIT051240472019-06-27T05:10:23Z http://ndltd.ncl.edu.tw/handle/44zu43 Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements 以基因演算法設計繞射光學元件之突變機制研究 Chih-Ming Lin 林錦明 碩士 國立臺北科技大學 光電工程系研究所 95 In this thesis, we study the mutation system of the genetic algorithm (GA) for optimization design of the binary phase diffractive optical elements (DOEs). The GA scheme adopted here was the simple genetic algorithm. We discussed the relationship between parameters of mutation system and convergent properties by the simulation results of binary DOEs that were designed using GA. We discussed the effects of the number of population, and the size of elements on the performance of optimization design. The number of population determined the population density of solution space. As the number of population increased, the mutation rate to obtain the highest efficiency of optimization design reduced. The size of elements governed the size of solution space. As the size of elements reduced, the final convergence value of the fitness function increased. Among the cases at simulated, the optimal mutation rate to achieve the best performance of the binary DOEs was obtained in 0.03% ~ 0.08%, depended on parameters discussed above. These rates correspond to 0.41 ~ 2.05 pixels per DOE, and give an important reference for the use of GA to design the DOEs. Wei-Feng Hsu 徐巍峰 2007 學位論文 ; thesis 94 zh-TW |
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碩士 === 國立臺北科技大學 === 光電工程系研究所 === 95 === In this thesis, we study the mutation system of the genetic algorithm (GA) for optimization design of the binary phase diffractive optical elements (DOEs). The GA scheme adopted here was the simple genetic algorithm. We discussed the relationship between parameters of mutation system and convergent properties by the simulation results of binary DOEs that were designed using GA. We discussed the effects of the number of population, and the size of elements on the performance of optimization design. The number of population determined the population density of solution space. As the number of population increased, the mutation rate to obtain the highest efficiency of optimization design reduced. The size of elements governed the size of solution space. As the size of elements reduced, the final convergence value of the fitness function increased. Among the cases at simulated, the optimal mutation rate to achieve the best performance of the binary DOEs was obtained in 0.03% ~ 0.08%, depended on parameters discussed above. These rates correspond to 0.41 ~ 2.05 pixels per DOE, and give an important reference for the use of GA to design the DOEs.
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author2 |
Wei-Feng Hsu |
author_facet |
Wei-Feng Hsu Chih-Ming Lin 林錦明 |
author |
Chih-Ming Lin 林錦明 |
spellingShingle |
Chih-Ming Lin 林錦明 Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements |
author_sort |
Chih-Ming Lin |
title |
Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements |
title_short |
Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements |
title_full |
Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements |
title_fullStr |
Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements |
title_full_unstemmed |
Study of Mutation System of Genetic Algorithm for Optimization Design of Diffractive Optical Elements |
title_sort |
study of mutation system of genetic algorithm for optimization design of diffractive optical elements |
publishDate |
2007 |
url |
http://ndltd.ncl.edu.tw/handle/44zu43 |
work_keys_str_mv |
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