Lee Sum Wai = 應用多目標基因演算法於合併三維破裂物件 / 李芯慧. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references. / Text in English; abstracts in English and Chinese. / Lee Sum Wai = Ying yong duo mu biao ji yin yan suan fa yu he bing san wei po lie wu jian / Li Xinhui. / Contents --- p.VI / List of Figures --- p.IX / List of Tables --- p.XIII / Chapter Chapter 1 --- Introduction --- p.1-1 / Chapter 1.1. --- A review of assembling objects --- p.1-3 / Chapter 1.1.1. --- Two-Dimensional matching --- p.1-3 / Chapter 1.1.2. --- Three-Dimensional matching --- p.1-4 / Chapter 1.1.3. --- 2.5-Dimensional matching --- p.1-5 / Chapter 1.2. --- Objectives of this research work --- p.1-7 / Chapter 1.2.1. --- Local Matching of fragments --- p.1-7 / Chapter 1.2.2. --- Global Matching fragments --- p.1-8 / Chapter 1.3. --- Thesis Outline --- p.1-9 / Chapter Chapter 2 --- Background Information --- p.2-1 / Chapter 2.1. --- Three-Dimensional Objects Representation --- p.2-1 / Chapter 2.2. --- Three-Dimensional Objects Geometric Transformation --- p.2-3 / Chapter 2.1.1. --- Translation --- p.2-4 / Chapter 2.1.2. --- Rotation --- p.2-5 / Chapter 2.3. --- Orientated Bounding Box (OBB) --- p.2-6 / Chapter 2.4. --- Scan-Line Method --- p.2-7 / Chapter 2.5. --- Mesh Simplification --- p.2-10 / Chapter 2.6. --- Review of the Surface Matching Method --- p.2-12 / Chapter 2.6.1. --- G. Papaioannou et al ´بs method --- p.2-13 / Chapter Chapter 3 --- Genetic Algorithm --- p.3-1 / General introduction --- p.3-1 / Chapter 3.1. --- Characteristics of Genetic Algorithms --- p.3-3 / Chapter 3.2. --- Mechanism of Genetic Algorithms --- p.3-4 / Chapter 3.2.1. --- Coding --- p.3-4 / Chapter 3.2.2. --- Reproduction --- p.3-5 / Chapter 3.2.3. --- Selection --- p.3-8 / Chapter 3.2.4. --- Stopping Criteria --- p.3-9 / Chapter 3.3. --- Convergence of Genetic Algorithms --- p.3-10 / Chapter 3.4. --- Comparison with Traditional Optimization Methods --- p.3-13 / Chapter 3.4.1. --- Test Function - Sphere --- p.3-14 / Chapter 3.4.2. --- Test Function - Rosenbrock's Saddle --- p.3-19 / Chapter 3.4.3. --- Test Function 一 Step --- p.3-22 / Chapter 3.4.4. --- Test Function -Quartic --- p.3-25 / Chapter 3.4.5. --- Test Function - Shekel's Foxholes --- p.3-28 / Chapter 3.5. --- Multi-Objective Genetic Algorithms --- p.3-29 / Chapter 3.5.1. --- Non-Pareto Approach --- p.3-31 / Chapter 3.5.2. --- Pareto-Ranking --- p.3-32 / Chapter 3.5.3. --- Comparison --- p.3-35 / Chapter Chapter 4 --- Assembling broken objects (I) --- p.4-1 / Chapter 4.1. --- System Flow of Single Pair Assemblage --- p.4-2 / Chapter 4.2. --- Parameterization --- p.4-3 / Chapter 4.2.1. --- Degree of Freedom --- p.4-3 / Chapter 4.2.2. --- Reference Plane and Sampling Points --- p.4-4 / Chapter 4.3. --- Matching Error --- p.4-5 / Chapter 4.3.1. --- Counterpart Surface Matching Error --- p.4-5 / Chapter 4.3.2. --- Border Matching Error --- p.4-7 / Chapter 4.4. --- Correlation-Based Matching Method --- p.4-14 / Chapter Chapter 5 --- Assembling Broken Objects (II)- Global Matching --- p.5-1 / Chapter 5.1. --- Arrangement Strategy --- p.5-2 / Chapter 5.1.1. --- Introduction to Packing --- p.5-2 / Chapter 5.1.2. --- Proposed Architecture --- p.5-6 / Chapter 5.2. --- Relational Multi-Objective Genetic Algorithm --- p.5-13 / Chapter 5.2.1. --- Existing Problem --- p.5-13 / Chapter 5.2.2. --- A New Operator --- p.5-14 / Chapter 5.2.3. --- Relationship Function --- p.5-16 / Chapter 5.3. --- Conclusion and summary --- p.5-20 / Chapter Chapter 6 --- Optimization Approach by Genetic Algorithm --- p.6-1 / Chapter 6.1. --- Solution Space --- p.6-1 / Chapter 6.2. --- Formulation of Gene and Chromosome --- p.6-3 / Chapter 6.2.1. --- Matching Three or More Fragments --- p.6-4 / Chapter 6.2.2. --- Matching Two Fragments --- p.6-5 / Chapter 6.3. --- Fitness Function --- p.6-5 / Chapter 6.3.1. --- Matching Two Fragments --- p.6-5 / Chapter 6.3.2. --- Matching Three or More Fragments --- p.6-6 / Chapter 6.4. --- Reproduction --- p.6-7 / Chapter 6.4.1. --- Crossover --- p.6-8 / Chapter 6.4.2. --- Mutation --- p.6-9 / Chapter 6.4.3. --- Inheritance --- p.6-9 / Chapter 6.5. --- Selection --- p.6-9 / Chapter Chapter 7 --- Experimental Results --- p.7-1 / Chapter 7.1 --- Data Acquisition --- p.7-1 / Chapter 7.2 --- Experiment for Mesh Simplification --- p.7-4 / Chapter 7.3 --- Experiment for Correlation-Based Matching Method --- p.7-5 / Chapter 7.4 --- Experiment One: Two Fragments --- p.7-6 / Chapter 7.5 --- Experiment Two: Several Fragments --- p.7-10 / Chapter 7.5.1 --- Constraint Direction Matching --- p.7-10 / Chapter 7.5.2 --- Unconstraint Direction Matching --- p.7-14 / Chapter Chapter 8 --- Conclusion --- p.8-1 / Appendix Reference --- p.1

Ng Sai-Cheong. / Thesis submitted in: December 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 172-178). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Problem Specifications and Motivations --- p.3 / Chapter 1.3 --- Contributions of the Thesis --- p.5 / Chapter 1.4 --- Thesis Roadmap --- p.6 / Chapter 2 --- Related Work --- p.9 / Chapter 2.1 --- Supervised Classification Techniques --- p.9 / Chapter 2.1.1 --- Classification Rules --- p.9 / Chapter 2.1.2 --- Decision Trees --- p.11 / Chapter 2.2 --- Evolutionary Algorithms --- p.19 / Chapter 2.2.1 --- Genetic Algorithms --- p.19 / Chapter 2.2.2 --- Genetic Programming --- p.24 / Chapter 2.2.3 --- Evolution Strategies --- p.26 / Chapter 2.2.4 --- Evolutionary Programming --- p.32 / Chapter 2.3 --- Applications of Evolutionary Algorithms to Induction of Classification Rules --- p.33 / Chapter 2.3.1 --- SCION --- p.33 / Chapter 2.3.2 --- GABIL --- p.34 / Chapter 2.3.3 --- LOGENPRO --- p.35 / Chapter 2.4 --- Applications of Evolutionary Algorithms to Construction of Decision Trees --- p.35 / Chapter 2.4.1 --- Binary Tree Genetic Algorithm --- p.35 / Chapter 2.4.2 --- OC1-GA --- p.36 / Chapter 2.4.3 --- OC1-ES --- p.38 / Chapter 2.4.4 --- GATree --- p.38 / Chapter 2.4.5 --- Induction of Linear Decision Trees using Strong Typing GP --- p.39 / Chapter 2.5 --- Spatial Data Structures and its Applications --- p.40 / Chapter 2.5.1 --- Spatial Data Structures --- p.40 / Chapter 2.5.2 --- Applications of Spatial Data Structures --- p.42 / Chapter 3 --- Induction of Classification Rules using Genetic Algorithms --- p.45 / Chapter 3.1 --- Introduction --- p.45 / Chapter 3.2 --- Rule Learning using Genetic Algorithms --- p.46 / Chapter 3.2.1 --- Population Initialization --- p.47 / Chapter 3.2.2 --- Fitness Evaluation of Chromosomes --- p.49 / Chapter 3.2.3 --- Token Competition --- p.50 / Chapter 3.2.4 --- Chromosome Elimination --- p.51 / Chapter 3.2.5 --- Rule Migration --- p.52 / Chapter 3.2.6 --- Crossover --- p.53 / Chapter 3.2.7 --- Mutation --- p.55 / Chapter 3.2.8 --- Calculating the Number of Correctly Classified Training Samples in a Rule Set --- p.56 / Chapter 3.3 --- Performance Evaluation --- p.56 / Chapter 3.3.1 --- Performance Comparison of the GA-based CPRLS and Various Supervised Classifi- cation Algorithms --- p.57 / Chapter 3.3.2 --- Performance Comparison of the GA-based CPRLS and RS-based CPRLS --- p.68 / Chapter 3.3.3 --- Effects of Token Competition --- p.69 / Chapter 3.3.4 --- Effects of Rule Migration --- p.70 / Chapter 3.4 --- Chapter Summary --- p.73 / Chapter 4 --- Genetic Algorithm-based Quadratic Decision Trees --- p.74 / Chapter 4.1 --- Introduction --- p.74 / Chapter 4.2 --- Construction of Quadratic Decision Trees --- p.76 / Chapter 4.3 --- Evolving the Optimal Quadratic Hypersurface using Genetic Algorithms --- p.77 / Chapter 4.3.1 --- Population Initialization --- p.80 / Chapter 4.3.2 --- Fitness Evaluation --- p.81 / Chapter 4.3.3 --- Selection --- p.81 / Chapter 4.3.4 --- Crossover --- p.82 / Chapter 4.3.5 --- Mutation --- p.83 / Chapter 4.4 --- Performance Evaluation --- p.84 / Chapter 4.4.1 --- Performance Comparison of the GA-based QDT and Various Supervised Classification Algorithms --- p.85 / Chapter 4.4.2 --- Performance Comparison of the GA-based QDT and RS-based QDT --- p.92 / Chapter 4.4.3 --- Effects of Changing Parameters of the GA-based QDT --- p.93 / Chapter 4.5 --- Chapter Summary --- p.109 / Chapter 5 --- Induction of Linear and Quadratic Decision Trees using Spatial Data Structures --- p.111 / Chapter 5.1 --- Introduction --- p.111 / Chapter 5.2 --- Construction of k-D Trees --- p.113 / Chapter 5.3 --- Construction of Generalized Quadtrees --- p.119 / Chapter 5.4 --- Induction of Oblique Decision Trees using Spatial Data Structures --- p.124 / Chapter 5.5. --- Induction of Quadratic Decision Trees using Spatial Data Structures --- p.130 / Chapter 5.6 --- Performance Evaluation --- p.139 / Chapter 5.6.1 --- Performance Comparison with Various Supervised Classification Algorithms --- p.142 / Chapter 5.6.2 --- Effects of Changing the Minimum Number of Training Samples at Each Node of a k-D Tree --- p.155 / Chapter 5.6.3 --- Effects of Changing the Minimum Number of Training Samples at Each Node of a Generalized Quadtree --- p.157 / Chapter 5.6.4 --- Effects of Changing the Size of Datasets . --- p.158 / Chapter 5.7 --- Chapter Summary --- p.160 / Chapter 6 --- Conclusions --- p.164 / Chapter 6.1 --- Contributions --- p.164 / Chapter 6.2 --- Future Work --- p.167 / Chapter A --- Implementation of Data Mining Algorithms Specified in the Thesis --- p.170 / Bibliography --- p.178

by Yong Liang. / "November 2003." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (p. 237-266). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Chun-pong Cheung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 48-51). / Chapter Chapter 1 --- INTRODUCTION --- p.1 / Chapter §.1.1 --- Toeplitz and Circulant Matrix --- p.1 / Chapter §1.2 --- Conjugate Gradient Method --- p.5 / Chapter §1.3 --- Outline of the Thesis --- p.9 / Chapter Chapter 2 --- INVERSE PROBLEM --- p.11 / Chapter §2.1 --- Inverse Problem --- p.11 / Chapter §2.2 --- Tikhonov Regularization --- p.12 / Chapter Chapter 3 --- FAST ALGORITHM ON THERMAL TOMOGRAPHY --- p.14 / Chapter §3.1 --- Introduction --- p.15 / Chapter §3.2 --- Linearization --- p.15 / Chapter §3.3 --- Regularization by the Identity Operator --- p.17 / Chapter §3.4 --- Regularization by the Laplacian Operator --- p.18 / Chapter §3.5 --- Preconditioning with the Laplacian --- p.21 / Chapter Chapter 4 --- COMPUTERIZED TOMOGRAPHY SCAN --- p.27 / Chapter §4.1 --- Projection Problem --- p.27 / Chapter §4.2 --- Radon Transform --- p.29 / Chapter §4.3 --- Reformulation of Projection Problem --- p.31 / Chapter §4.4 --- Numerical Experiments --- p.35 / Chapter §4.5 --- Sirhplification of Formula --- p.41 / References --- p.48

Lee Wai Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 108-115). / Chapter 1 --- Introduction --- p.8 / Chapter 1.1 --- Introduction to Group Technology --- p.8 / Chapter 1.2 --- Cell design --- p.9 / Chapter 1.3 --- Objectives of the research --- p.11 / Chapter 1.4 --- Organization of thesis --- p.11 / Chapter 2 --- Literature review --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- Standard models --- p.14 / Chapter 2.2.1 --- Array-based methods --- p.16 / Chapter 2.2.2 --- Cluster identification --- p.16 / Chapter 2.2.3 --- Graph-based methods --- p.17 / Chapter 2.2.4 --- Integer programming --- p.17 / Chapter 2.2.5 --- Seed-based --- p.18 / Chapter 2.2.6 --- Similarity coefficient --- p.18 / Chapter 2.2.7 --- Artificial intelligence methods --- p.19 / Chapter 2.3 --- Generalized models --- p.19 / Chapter 2.3.1 --- Machine assignment models --- p.20 / Chapter 2.3.2 --- Part family models --- p.20 / Chapter 2.3.3 --- Cell formation models --- p.21 / Chapter 3 --- Genetic cell formation algorithm --- p.22 / Chapter 3.1 --- Introduction --- p.22 / Chapter 3.2 --- TSP formulation for a permutation of machines --- p.23 / Chapter 3.3 --- Genetic algorithms --- p.26 / Chapter 3.3.1 --- Representation and basic crossover operators --- p.27 / Chapter 3.3.2 --- Fitness function --- p.28 / Chapter 3.3.3 --- Initialization --- p.29 / Chapter 3.3.4 --- Parent selection strategies --- p.30 / Chapter 3.3.5 --- Crossover --- p.31 / Chapter 3.3.6 --- Mutation --- p.37 / Chapter 3.3.7 --- Replacement --- p.38 / Chapter 3.3.8 --- Termination --- p.38 / Chapter 3.4 --- Formation of machine cells and part families --- p.39 / Chapter 3.4.1 --- Objective functions --- p.39 / Chapter 3.4.2 --- Machine assignment --- p.42 / Chapter 3.4.3 --- Part assignment --- p.43 / Chapter 3.5 --- Implementation --- p.43 / Chapter 3.6 --- An illustrative example --- p.45 / Chapter 3.7 --- Comparative Study --- p.49 / Chapter 3.8 --- Conclusions --- p.50 / Chapter 4 --- A multi-chromosome GA for minimizing total intercell and intracell moves --- p.55 / Chapter 4.1 --- Introduction --- p.55 / Chapter 4.2 --- The model --- p.57 / Chapter 4.3 --- Solution techniques to the workload model --- p.61 / Chapter 4.3.1 --- Logendran's original approach --- p.62 / Chapter 4.3.2 --- Standard representation - the GA approach --- p.63 / Chapter 4.3.3 --- Multi-chromosome representation --- p.65 / Chapter 4.4 --- Comparative Study --- p.70 / Chapter 4.4.1 --- Problem 1 --- p.70 / Chapter 4.4.2 --- Problem 2 --- p.71 / Chapter 4.4.3 --- Problem 3 --- p.75 / Chapter 4.4.4 --- Problem 4 --- p.76 / Chapter 4.5 --- Bi-criteria Model --- p.79 / Chapter 4.5.1 --- Experimental results --- p.85 / Chapter 4.6 --- Conclusions --- p.85 / Chapter 5 --- Integrated design of cellular manufacturing systems in the presence of alternative process plans --- p.88 / Chapter 5.1 --- Introduction --- p.88 / Chapter 5.1.1 --- Literature review --- p.90 / Chapter 5.1.2 --- Motivation --- p.92 / Chapter 5.2 --- Mathematical models --- p.93 / Chapter 5.2.1 --- Notation --- p.93 / Chapter 5.2.2 --- Objective functions --- p.95 / Chapter 5.3 --- Our solution --- p.96 / Chapter 5.4 --- Illustrative example and analysis of results --- p.98 / Chapter 5.4.1 --- Solution for objective function 1 --- p.101 / Chapter 5.4.2 --- Solution for objective function 2 --- p.102 / Chapter 5.5 --- Conclusions --- p.103 / Chapter 6 --- Conclusions --- p.104 / Chapter 6.1 --- Summary of achievements --- p.104 / Chapter 6.2 --- Future works --- p.106

by Wing-Fai Ng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 7-8). / Abstract --- p.1-2 / Introduction --- p.3-6 / References --- p.7-8 / Paper I --- p.9-32 / Paper II --- p.33-60

by Wong Yin Bun, Terence. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 99-104). / Abstract also in Chinese. / List of Figures --- p.ix / List of Tables --- p.xi / Chapter I --- Preliminary --- p.1 / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Thesis themes --- p.4 / Chapter 1.1.1 --- Dynamical view of landscape --- p.4 / Chapter 1.1.2 --- Bottom-up self-feedback algorithm with memory --- p.4 / Chapter 1.1.3 --- Cooperation and competition --- p.5 / Chapter 1.1.4 --- Contributions to genetic algorithms --- p.5 / Chapter 1.2 --- Thesis outline --- p.5 / Chapter 1.3 --- Contribution at a glance --- p.6 / Chapter 1.3.1 --- Problem --- p.6 / Chapter 1.3.2 --- Approach --- p.7 / Chapter 1.3.3 --- Contributions --- p.7 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Iterative stochastic searching algorithms --- p.8 / Chapter 2.1.1 --- The algorithm --- p.8 / Chapter 2.1.2 --- Stochasticity --- p.10 / Chapter 2.2 --- Fitness landscapes and its relation to neighborhood --- p.12 / Chapter 2.2.1 --- Direct searching --- p.12 / Chapter 2.2.2 --- Exploration and exploitation --- p.12 / Chapter 2.2.3 --- Fitness landscapes --- p.13 / Chapter 2.2.4 --- Neighborhood --- p.16 / Chapter 2.3 --- Species formation methods --- p.17 / Chapter 2.3.1 --- Crowding methods --- p.17 / Chapter 2.3.2 --- Deterministic crowding --- p.18 / Chapter 2.3.3 --- Sharing method --- p.18 / Chapter 2.3.4 --- Dynamic niching --- p.19 / Chapter 2.4 --- Summary --- p.21 / Chapter II --- Probabilistic Binary Hierarchical Search --- p.22 / Chapter 3 --- The basic algorithm --- p.23 / Chapter 3.1 --- Introduction --- p.23 / Chapter 3.2 --- Search space reduction with binary hierarchy --- p.25 / Chapter 3.3 --- Search space modeling --- p.26 / Chapter 3.4 --- The information processing cycle --- p.29 / Chapter 3.4.1 --- Local searching agents --- p.29 / Chapter 3.4.2 --- Global environment --- p.30 / Chapter 3.4.3 --- Cooperative refinement and feedback --- p.33 / Chapter 3.5 --- Enhancement features --- p.34 / Chapter 3.5.1 --- Fitness scaling --- p.34 / Chapter 3.5.2 --- Elitism --- p.35 / Chapter 3.6 --- Illustration of the algorithm behavior --- p.36 / Chapter 3.6.1 --- Test problem --- p.36 / Chapter 3.6.2 --- Performance study --- p.38 / Chapter 3.6.3 --- Benchmark tests --- p.45 / Chapter 3.7 --- Discussion and analysis --- p.45 / Chapter 3.7.1 --- Hierarchy of partitions --- p.45 / Chapter 3.7.2 --- Availability of global information --- p.47 / Chapter 3.7.3 --- Adaptation --- p.47 / Chapter 3.8 --- Summary --- p.48 / Chapter III --- Cooperation and Competition --- p.50 / Chapter 4 --- High-dimensionality --- p.51 / Chapter 4.1 --- Introduction --- p.51 / Chapter 4.1.1 --- The challenge of high-dimensionality --- p.51 / Chapter 4.1.2 --- Cooperation - A solution to high-dimensionality --- p.52 / Chapter 4.2 --- Probabilistic Cooperative Binary Hierarchical Search --- p.52 / Chapter 4.2.1 --- Decoupling --- p.52 / Chapter 4.2.2 --- Cooperative fitness --- p.53 / Chapter 4.2.3 --- The cooperative model --- p.54 / Chapter 4.3 --- Empirical performance study --- p.56 / Chapter 4.3.1 --- pBHS versus pcBHS --- p.56 / Chapter 4.3.2 --- Scaling behavior of pcBHS --- p.60 / Chapter 4.3.3 --- Benchmark test --- p.62 / Chapter 4.4 --- Summary --- p.63 / Chapter 5 --- Deception --- p.65 / Chapter 5.1 --- Introduction --- p.65 / Chapter 5.1.1 --- The challenge of deceptiveness --- p.65 / Chapter 5.1.2 --- Competition: A solution to deception --- p.67 / Chapter 5.2 --- Probabilistic cooperative-competitive binary hierarchical search --- p.67 / Chapter 5.2.1 --- Overview --- p.68 / Chapter 5.2.2 --- The cooperative-competitive model --- p.68 / Chapter 5.3 --- Empirical performance study --- p.70 / Chapter 5.3.1 --- Goldberg's deceptive function --- p.70 / Chapter 5.3.2 --- "Shekel family - S5, S7, and S10" --- p.73 / Chapter 5.4 --- Summary --- p.74 / Chapter IV --- Finale --- p.78 / Chapter 6 --- A new genetic operator --- p.79 / Chapter 6.1 --- Introduction --- p.79 / Chapter 6.2 --- Variants of the integration --- p.80 / Chapter 6.2.1 --- Fixed-fraction-of-all --- p.83 / Chapter 6.2.2 --- Fixed-fraction-of-best --- p.83 / Chapter 6.2.3 --- Best-from-both --- p.84 / Chapter 6.3 --- Empricial performance study --- p.84 / Chapter 6.4 --- Summary --- p.88 / Chapter 7 --- Conclusion and Future work --- p.89 / Chapter A --- The pBHS Algorithm --- p.91 / Chapter A.1 --- Overview --- p.91 / Chapter A.2 --- Details --- p.91 / Chapter B --- Test problems --- p.96 / Bibliography --- p.99

No description available.

Cheung, Ho Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 91-96). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Matroids and Matrices --- p.5 / Chapter 2.1.1 --- Examples --- p.6 / Chapter 2.1.2 --- Constructions --- p.7 / Chapter 2.1.3 --- Matroid Intersection --- p.8 / Chapter 2.1.4 --- Matroid Parity --- p.9 / Chapter 2.2 --- Matrix Formulations --- p.14 / Chapter 2.2.1 --- Graph Matching --- p.15 / Chapter 2.2.2 --- Skew-Symmetric Matrix --- p.16 / Chapter 2.2.3 --- Linear Matroid Parity --- p.21 / Chapter 2.2.4 --- Weighted Problems --- p.25 / Chapter 2.3 --- Algebraic Tools --- p.26 / Chapter 2.3.1 --- Matrix Algorithms --- p.26 / Chapter 2.3.2 --- Computing Matrix Inverse --- p.28 / Chapter 2.3.3 --- Matrix of Indeterminates --- p.32 / Chapter 2.3.4 --- Mixed Skew-symmetric Matrix --- p.34 / Chapter 2.4 --- Algebraic Algorithms for Graph Matching --- p.35 / Chapter 2.4.1 --- Matching in O{nw+1) time --- p.36 / Chapter 2.4.2 --- Matching in O(n3) time --- p.37 / Chapter 2.4.3 --- Matching in O(nw) time --- p.38 / Chapter 2.4.4 --- Weighted Algorithms --- p.39 / Chapter 2.4.5 --- Parallel Algorithms --- p.40 / Chapter 2.5 --- Algebraic Algorithms for Graph Connectivity --- p.41 / Chapter 2.5.1 --- Previous Approach --- p.41 / Chapter 2.5.2 --- Matrix Formulation Using Network Coding --- p.42 / Chapter 3 --- Linear Matroid Parity --- p.49 / Chapter 3.1 --- Introduction --- p.49 / Chapter 3.1.1 --- Problem Formulation and Previous Work --- p.50 / Chapter 3.1.2 --- Our Results --- p.52 / Chapter 3.1.3 --- Techniques --- p.55 / Chapter 3.2 --- Preliminaries --- p.56 / Chapter 3.3 --- A Simple Algebraic Algorithm for Linear Matroid Parity --- p.56 / Chapter 3.3.1 --- An 0(mr2) Algorithm --- p.56 / Chapter 3.4 --- Graph Algorithms --- p.59 / Chapter 3.4.1 --- Mader's S-Path --- p.59 / Chapter 3.4.2 --- Graphic Matroid Parity --- p.64 / Chapter 3.4.3 --- Colorful Spanning Tree --- p.66 / Chapter 3.5 --- Weighted Linear Matroid Parity --- p.69 / Chapter 3.6 --- A Faster Linear Matroid Parity Algorithm --- p.71 / Chapter 3.6.1 --- Matrix Formulation --- p.71 / Chapter 3.6.2 --- An O(mw) Algorithm --- p.74 / Chapter 3.6.3 --- An O(mrw - 1 ) Algorithm --- p.76 / Chapter 3.7 --- Maximum Cardinality Matroid Parity --- p.79 / Chapter 3.8 --- Open Problems --- p.80 / Chapter 4 --- Graph Connectivities --- p.81 / Chapter 4.1 --- Introduction --- p.81 / Chapter 4.2 --- Inverse of Well-Separable Matrix --- p.83 / Chapter 4.3 --- Directed Graphs with Good Separators --- p.86 / Chapter 4.4 --- Open Problems --- p.89

The design profession is responding to the complex systems represented by architecture and planning by increasingly incorporating the power of computer technology into the design process. This represents a paradigm shift, and requires that designers rise to the challenge of both embracing modern technologies to perform increasingly sophisticated tasks without compromising their objective to create meaningful and environmentally sensitive architecture. This dissertation investigated computer-based fractal tools applied within a traditional architectural charette towards a design process with the potential to address the complex issues architects and planners face today. We developed and presented an algorithm that draws heavily from fractal mathematics and fractal theory. Fractals offer a quantitative and qualitative relation between nature, the built environment and computational mechanics and in this dissertation serve as a bridge between these realms. We investigated how qualitative/quantitative fractal tools may inform an architectural design process both in terms of generative formal solutions as well as a metric for assessing the complexity of designs and historic architecture. The primary research objective was to develop a compelling cybernetic design process and apply it to a real-world and multi-faceted case study project within a formal architectural critique. Jurors were provided a platform for evaluating design work and weighing in as practicing professional architects. Jurors' comments were documented and discussed and presented as part of the dissertation. Our intention was to open up the discussion and document the effectiveness or ineffectiveness of the process we presented. First we discussed the history of generative and algorithmic design and fractals in architecture. We begin with examples in ancient Hindu temple architecture as well as Middle Eastern architecture and Gothic as well as Art Nouveau. We end this section with a discussion of fractals in the contemporary architecture of Frank Lloyd Wright and the Organic school. Next we developed a cybernetic design process incorporating a computer-based tool termed DBVgen within a closed loop designer/algorithm back and forth. The tool we developed incorporated a genetic algorithm that used fractal dimension as the primary fitness criterion. We applied our design process with mixed results as discussed by the jurors whose feedback was chunked into ten categories and assessed along with the author/designer's feedback. Generally we found that compelling designs tended to have a higher FD, whereas, the converse was not true that higher FD consistently led to more compelling designs. Finally, we further developed fractal theory towards an appropriate consideration of the significance of fractals in architecture. We articulated a nuanced definition of fractals in architecture as: designs having multi-scale and multi-functional representations of some unifying organizing principle as the result of an iterative process. We then wrapped this new understanding of fractals in architecture to precedent relevant to the case study project. We present and discuss fractals in the work of Frank Lloyd Wright as well as Dean Bryant Vollendorf. We expand on how a theory of fractals used in architecture may continue to be developed and applied as a critical tool in analyzing historic and contemporary architecture as well as a creative framework for designing new architectural solutions to better address the complex world we live in.