Efficient-Genetic Algorithm for Engineering and Management Applications
博士 === 國立中央大學 === 資訊管理學系 === 103 === Evolutionary computations have been widely used in many real word problems. In particular, evolutionary algorithms can be considered as the global optimization methods with a met heuristic or stochastic optimization character and they are widely applied for blac...
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ndltd-TW-103NCU053960582016-05-22T04:41:04Z http://ndltd.ncl.edu.tw/handle/18628492578549405968 Efficient-Genetic Algorithm for Engineering and Management Applications 改良式快速基因演算法: 工程與管理之應用 Zong-Yao Chen 陳宗堯 博士 國立中央大學 資訊管理學系 103 Evolutionary computations have been widely used in many real word problems. In particular, evolutionary algorithms can be considered as the global optimization methods with a met heuristic or stochastic optimization character and they are widely applied for black box problems (no derivatives known) and non-deterministic polynomial-time hard problems (NP-hard), often in the context of expensive optimization. However, their computational complexities are very high leading to the major limitation in practice. In this dissertation, we introduce a novel Efficient-Genetic Algorithm (EGA), which fits “biological evolution” into the evolutionary process. In other words, after long-term evolution, individuals find the most efficient way to allocate resources and evolve. There are two experiments to validate the EGA. The first experimental study is based on a scheduling problem, and two state-of-the-art algorithms including Genetic algorithm (GA) and Immunity algorithm (IA) are compared with EGA. The second one focuses on the data reduction problem where four very high dimensional datasets are used. In addition, four state-of-the-art algorithms including IB3, DROP3, ICF, and GA are compared with EGA. Chih-Fong Tsai 蔡志豐 2014 學位論文 ; thesis 87 zh-TW |
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博士 === 國立中央大學 === 資訊管理學系 === 103 === Evolutionary computations have been widely used in many real word problems. In particular, evolutionary algorithms can be considered as the global optimization methods with a met heuristic or stochastic optimization character and they are widely applied for black box problems (no derivatives known) and non-deterministic polynomial-time hard problems (NP-hard), often in the context of expensive optimization. However, their computational complexities are very high leading to the major limitation in practice.
In this dissertation, we introduce a novel Efficient-Genetic Algorithm (EGA), which fits “biological evolution” into the evolutionary process. In other words, after long-term evolution, individuals find the most efficient way to allocate resources and evolve.
There are two experiments to validate the EGA. The first experimental study is based on a scheduling problem, and two state-of-the-art algorithms including Genetic algorithm (GA) and Immunity algorithm (IA) are compared with EGA. The second one focuses on the data reduction problem where four very high dimensional datasets are used. In addition, four state-of-the-art algorithms including IB3, DROP3, ICF, and GA are compared with EGA.
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author2 |
Chih-Fong Tsai |
author_facet |
Chih-Fong Tsai Zong-Yao Chen 陳宗堯 |
author |
Zong-Yao Chen 陳宗堯 |
spellingShingle |
Zong-Yao Chen 陳宗堯 Efficient-Genetic Algorithm for Engineering and Management Applications |
author_sort |
Zong-Yao Chen |
title |
Efficient-Genetic Algorithm for Engineering and Management Applications |
title_short |
Efficient-Genetic Algorithm for Engineering and Management Applications |
title_full |
Efficient-Genetic Algorithm for Engineering and Management Applications |
title_fullStr |
Efficient-Genetic Algorithm for Engineering and Management Applications |
title_full_unstemmed |
Efficient-Genetic Algorithm for Engineering and Management Applications |
title_sort |
efficient-genetic algorithm for engineering and management applications |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/18628492578549405968 |
work_keys_str_mv |
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