改良式快速基因演算法: 工程與管理之應用

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陳宗堯(Zong-Yao Chen) 查詢紙本館藏

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資訊管理學系

論文名稱

改良式快速基因演算法: 工程與管理之應用
(Efficient-Genetic Algorithm for Engineering and Management Applications)

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摘要(中)

進化式演算法早已經受到廣泛的應用，這類方法擁有的特性非常適合用來處理一些問題，例如黑箱與NP-hard (Non-deterministic Polynomial-time hard)的問題.
然而可惜的是，這類方法已經被證實屬於耗時(time consuming)的方法，這與實務上的需求是有差異的，例如: 工廠不可能花費一整天的時間等待一個最佳的排程、企業不可能耗費1個月進行資料的精簡。因此，縮短這類演算法的運算時間是極為重要的。
本研究提出了一個新穎的最佳化演算法名為快速基因演算法(Efficient-Genetic Algorithm, EGA), 透過增加更多的生物學的原理來改善演算法的演化效率。換句話說，在有限的資源下，生物經過了長時間的演化，找出了最有效率的演化以及存活方法，這是值得參考的。
本文透過兩個主要的實驗來驗證EGA的有效性，在第一個實驗中，針對EGA測試了兩個常見的最佳化問題。此外，本研究也納入了兩個知名的演算法分別是基因演算法(Genetic algorithm, GA)以及其改良的變種版本免疫演算法(Immunity algorithm, IA)。然而在第二個實驗中，為了貼近實務的需求，本研究採用了四個高維度的資料集進行資料縮減的實驗，並與四個知名的方法進行比較，四種方法分別為IB3、DROP3、ICF 還有GA。

摘要(英)

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.

關鍵字(中)

★ 資料縮減
★ 作業排程
★ 資料探勘
★ 機器學習
★ 人工智慧
★ 基因演算法
★ 高維度資料

關鍵字(英)

★ data mining
★ data reduction
★ genetic algorithms
★ high dimensional data
★ machine learning
★ scheduling problems

論文目次

中文提要……………………………………………………………………………………………………… i
英文提要……………………………………………………………………………………………………… ii
誌謝………………………………………………………………………………………………………………… iii
目錄……………………………………………………………………………………………………………… iv
表目錄…………………………………………………………………………………………………………… v
圖目錄………………………………………………………………………………………………………… vii
符號說明……………………………………………………………………………………………………… viii
1.Introduction…………………………………………………………………………………… - 1 -
1.1 Background Evolutionary Computation………………… - 2 -
1.2 Problem Defined……………………………………………………………………… - 3 -
1.3 Differences and Contributions………………………………… - 4 -
1.4 Dissertation Architecture…………………………………………… - 5 -
2. Literature Review…………………………………………………………………… - 6 -
2.1 Evolutionary Algorithms………………………………………………… - 6 -
2.2 The Genetic Algorithms…………………………………………………… - 9 -
I. Applications:……………………………………………………………………… - 9 -
II. Improvement:……………………………………………………………………… - 9 -
2.2.1 Improvement – Encoding……………………………………………… - 9 -
2.2.2 Improvement – Parameter Optimization………… - 10 -
2.2.3 Improvement - Operation Mode/flow………………… - 10 -
2.2.4 Summary of Genetic Algorithms…………………………… - 11 -
2.2.5 Application of GA in Surface Mount Technology (SMT)………………………………………………………………………………………………………………………… - 12 -
2.2.2 Application of GA in Data Reduction…………… - 13 -
2.3 Problem Background……………………………………………………………… - 14 -
2.3.1 Data Reduction…………………………………………………………………… - 14 -
2.3.2 Operations Scheduling………………………………………………… - 15 -
3. The Efficient Genetic Algorithm……………………………… - 17 -
3.1 The Basic Concept………………………………………………………………… - 17 -
3.2 Novel Features of EGA……………………………………………………… - 18 -
3.2.1 Reasonable Convergence……………………………………………… - 18 -
3.2.2 Nonlinear Adaptability……………………………………………… - 20 -
3.2.3 Genetic King – Inter-Generational Mating - 22 -
3.2.4 Hardy-Weinberg law………………………………………………………… - 23 -
3.2.5 Great Migration………………………………………………………………… - 24 -
3.2.6 New Generation…………………………………………………………………… - 25 -
3.3 A Small Running Example of Data Reduction Problem……………………………………………………………………………………………………… - 25 -
3.4 Pilot Test…………………………………………………………………………………… - 31 -
3.4.1 Mathematical Optimization……………………………………… - 31 -
3.4.2 Small Scale Data Reduction Problem……………… - 36 -
3.5 Discussion…………………………………………………………………………………… - 42 -
3.5.1 Evaluation and New Generation…………………………… - 42 -
3.5.2 Selection of Kings and Mating…………………………… - 43 -
3.5.3 Great Migration………………………………………………………………… - 45 -
3.5.4 Time Complexity Analysis………………………………………… - 46 -
4. Experiments…………………………………………………………………………………… - 48 -
4.1 Experiments I: High Dimensional Data Reduction………………………………………………………………………………………………… - 48 -
4.1.1 Classification Accuracy…………………………………………… - 49 -
4.1.2 Storage Requirements…………………………………………………… - 51 -
4.1.3 Computational Cost………………………………………………………… - 52 -
4.1.4 Discussion of High Dimensional Data Reduction………………………………………………………………………………………………… - 53 -
4.2 Experiments II: Scheduling Problem…………………… - 55 -
4.2.1 Moving the placement machine:…………………………… - 57 -
4.2.2 Switch machine:………………………………………………………………… - 57 -
4.2.3 Experimental setup………………………………………………………… - 58 -
4.2.4 Experimental result……………………………………………………… - 59 -
4.2.5 Discussion of Scheduling Problem…………………… - 61 -
5. Conclusion……………………………………………………………………………………… - 62 -
Referance………………………………………………………………………………………………… - 65 -
Appendix 1 - Pseudo code of EGA……………………………………… - 74 -

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指導教授

蔡志豐(Chih-Fong Tsai)

審核日期

2015-7-21

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