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研究生:許展碩
研究生(外文):Chan-Shuo Hsu
論文名稱:一個複合型的演化式計算:基因演算法結和約略集合理論
論文名稱(外文):A Hybrid Evolutionary Computing: Genetic Algorithm Incorporated with Rough Set Theory
指導教授:黃俊哲黃俊哲引用關係
指導教授(外文):Chun-Che Huang
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:71
中文關鍵詞:演化式計算基因演算法約略集合理論
外文關鍵詞:Evolutionary computing (EC)Genetic algorithm (GA)rough set
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演化式計算包括演化式規劃、演化式策略、基因演算法、基因規劃等,已經被廣泛的使用來解決最佳化的問題,尤其在大規模且複雜的系統中尋找最佳解。然而,這類的演算法,如果沒有整合足夠的知識,在演化的過程中,將會增加演化成本並影響演化效能及收斂的情形。而且一般的基因演算法只針對某一問題而設計,如果遇到另一個問題,就產生重新計設的成本。有鑑於此,本篇論文提出一般化的基因演算法,並整合由約略集合所產生的知識規則。只要能將一個問題分解成多個功能需求,就可以用一般化的基因演算法加以解決,而且整合由約略集合理論所發掘出來的知識,確實能提高此基因演算法的效能以及加速收斂,特別是用在簡化母體或限制交配這二方面。
The evolutionary computing (EC) comprises techniques of evolutionary programming, evolution strategies, genetic algorithms, and genetic programming. It has been widely used to solve optimization problems for large scale and complex systems. However, (i) without incorporating with sufficient knowledge, it does have a significant impact on the efficiency of the search for an optimal solution. (ii) In addition, the genetic algorithm in previous literatures is modeled to solve one problem exactly. It is required redesign cost for GA to apply to another problem. Due to the two reasons, this thesis develops a generic genetic algorithm incorporating with the knowledge extracted from the rough set theory. The advantages of the proposed solution approach includes: (i) it is modeled to solve generic problems which are decomposed into functional requirements, (ii) by incorporating the rough set theory, the discovered knowledge can contribute to improving the performance and efficiency of the proposed GA, specifically on initial population and constraining crossover.
Table of Contents
Acknowledgement ………………………………………………… i
Chinese Abstract………………………………………………… ii
Abstract ………………………………………………………… iii
Table of Content ……………………………………………… iv
List of Figures ……………………………………………… vi
List of Tables ………………………………………………… viii
Chapter 1. Introduction ……………………………………… 1
Chapter 2. Algorithm Development of the rough set theory …4
2.1. The Reduct Generation Procedure ……… 5
2.2. The rule identification algorithm with weight
analysis …………………………………… 6
2.3. The weight analysis for final reduct rules
…………………………………………………… 7
Chapter 3. The generic genetic algorithm incorporates with the
rough set theory ………………………………… 9
3.1. Symbols and notation ……………………… 11
3.2. Representation ……………………………… 12
3.3. Objective function ………………………… 13
3.4. Evolutionary operators and process …… 14
Chapter 4. The application of the proposed solution approach and an illustrative example ……………………………… 21
4.1. The application …………………………… 21
4.2. An illustrative example: PC synthesis … 26
4.2.1 The application of the rough set theory
…………………………………………… 26
4.2.2 The process of this case solved by the
roposed solution approach ………… 32
4.3. The other ways for the rough set to incorporate
with the GA ………………………………… 43
4.3.1 The rough set incorporated with the GA by
constraining crossover ……………… 43
4.3.2 The rough set incorporated with the GA by
reducing initial population and constraining
crossover ………………………………… 46
4.4. Discussion …………………………………… 48
Chapter 5. Conclusions ……………………………………… 50
References ……………………………………………………… 51
Appendix A ……………………………………………………… 56
List of Figures
Figure 3-1 The flow chart of the generic GA
incorporated with the rough set theory
……………………………………………… 10
Figure 3-2 An illustrative example of the functional
requirement hierarchy ………………… 11
Figure 3-3 Crossover operator …………………… 14
Figure 3-4 Crossover process ……………………… 15
Figure 3-5 Mutation operator ……………………… 15
Figure 3-6 Mutation process ……………………… 15
Figure 3-7 The flowchart of the proposed solution
approach ………………………………… 17
Figure 4-1 The matrix of the rule identification
……………………………………………… 29
Figure 4-2 The corresponding weights of the matrix in
Figure 4-1 ……………………………… 30
Figure 4-3 The Convergence of the porposed GA
……………………………………………… 41
Figure 4-4 The comparison of convergence between the
proposed GA and standard GA ………… 42
Figure 4-5 The convergence of the proposed GA
(constrain crossover) ………………… 45
Figure 4-6 The comparison of convergence between the
proposed GA (constrain crossover) and the
standard GA ……………………………… 46
Figure 4-7 The convergence of the proposed GA (reduce
initial population and constrain crossover)
……………………………………………… 47
Figure 4-8 The comparison of convergence between the
proposed GA (reduce initial population and
constrain crossover) and the standard GA
……………………………………………… 48
Figure A-1. The matrix of the rule identification
……………………………………………… 56
Figure A-2. The corresponding weight of matrix in
Figure A-1 ……………………………… 57
Figure A-3. The next step of Figure A-2 ………… 58
Figure A-4. The next step of Figure A-3 ………… 59
Figure A-5. The next step of Figure A-4 ………… 60
Figure A-6. The next step of Figure A-5 ………… 61
Figure A-7. The next step of Figure A-6 ………… 62
List of Tables
Table 4-1 A list of examples solved by the proposed
solution approach ……………………… 21
Table 4-2 The description of all attributes in
Table 4-3 ………………………………… 26
Table 4-3 The partial original data source of the PC
synthesis ………………………………… 27
Table 4-4 The value of reducts for the data in
Table 4-3 ………………………………… 27
Table 4-5 Expanded with weights of Table 4-3 …28
Table 4-6 The counted weights of each entry in each
reduct …………………………………… 29
Table 4-7 The final value reducts ……………… 32
Table 4-8 The explanations of reduct rules in Table 4.5
……………………………………………… 32
Table 4-9 Expanded with weights of Table 4-7 …33
Table 4-10 The weight of each entry of reduct rules
……………………………………………… 34
Table 4-11 The reduced initial population …… 34
Table 4-12 The Feasible Subset of the CPU …… 36
Table 4-13 Initialization population whose domain is
constrained by reduct rules ………… 37
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