臺灣博碩士論文加值系統

Most algorithms are evolved from a lot of modifications and improvements to become more precise. If we can decompose the essential of an algorithm and realize the reusability and expansibility, it would be possible to accelerate the algorithm’s improvement, reduce the complexity when programming, cut down the maintenance and decrease the possibility to re-program the code.
Aiming at this objective, we select the variable selection algorithm as the problem conveyer. There are a lot of possible modifications and significances in the variable selection process. We want to drill through the individual algorithm instead of combination of algorithms. We also want to overcome the challenge to minimize the IT effort and achieve several design goals, such as (G1) reusable component, (G2) flexible configuration and (G3) analyzable data abstraction.
This research aims at developing an algorithm reengineering methodology. The purpose is to provide a mechanism for effective algorithm evolutions, where the inputs are (I1) what to change and (I2) what to change to, while the outputs are (O1) where to change and (O2) how to change. The mechanism is based on the object-oriented analysis and design, a four-stage methodology is proposed for the effective algorithm evolutions: (S1) domain-independent module abstraction, (S2) domain-dependent function decomposition, (S3) objects extraction and configuration, and (S4) strategy design and derivation for effective algorithm evolutions. Finally, we design several generations to demonstrate and validate the proposed reengineering methodology for effective algorithm evolutions.

Acknowledgement i
Abstract ii
Contents iii
Contents of Figures v
Contents of Tables vi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Conveyer Problem 3
1.3 Research Scope and Methodology 7
Chapter 2 Domain-independent Module Derivation 9
2.1 Fundamental elements of an algorithm 9
2.2 Data Perspective 12
2.2.1 Data Module 12
2.2.2 Parameter Module 13
2.3 Procedure Perspective 14
2.3.1 Math Module 16
2.3.2 Logic Module 16
Chapter 3 Domain-dependent Function Decomposition 18
3.1 Flow Chart Introduction 18
3.2 Flow Decomposition Example 19
3.3 Flow Decomposition Process 21
Chapter 4 Object Derivation by Mapping Functions-Modules 26
4.1 Information technique for enabling algorithm configuration 26
4.1.1 Layered Architecture 26
4.1.2 Object-oriented Methodology 27
4.1.3 Strategy Design Pattern 28
4.2 Match Function Flow and Modules 29
4.3 Object Derivation 32
4.4 Match Function Flow and Objects 38
4.5 Data Module Discussion 42
Chapter 5 Strategy Derivation for Effective Algorithm Evolution 46
5.1 Effective algorithm evolution 46
5.2 Design of generations 47
5.2.1 Generation 1: Forward Selection 48
5.2.2 Generation 2: Backward Elimination 53
5.2.3 Generation 3: Stepwise Selection 64
5.2.4 Generation 4: Robust Stepwise Selection 69
5.3 Summary of generations 78
Chapter 6 Conclusions 81
6.1 Conclusions 81
6.2 Future Research 81

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