Fok Ka Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 96-101). / Abstracts in English and Chinese. / Abstract --- p.1 / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Evolutionary Computation --- p.1 / Chapter 1.2 --- Graphics Processing Unit --- p.2 / Chapter 1.3 --- Objective --- p.3 / Chapter 1.4 --- Contribution --- p.4 / Chapter 1.5 --- Thesis Organization --- p.4 / Chapter 2 --- Evolutionary Computation --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- General Framework --- p.7 / Chapter 2.3 --- Features of Evolutionary Algorithm --- p.8 / Chapter 2.3.1 --- Widely Applicable --- p.8 / Chapter 2.3.2 --- Parallelism --- p.9 / Chapter 2.3.3 --- Robust to Change --- p.9 / Chapter 2.4 --- Parallel and Distributed Evolutionary Algorithm --- p.9 / Chapter 2.4.1 --- Global Parallel Evolutionary Algorithms --- p.10 / Chapter 2.4.2 --- Fine-Grained Evolutionary Algorithms --- p.11 / Chapter 2.4.3 --- Island Distributed Evolutionary Algorithms --- p.12 / Chapter 2.5 --- Summary --- p.14 / Chapter 3 --- Graphics Processing Unit --- p.15 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.2 --- History of GPU --- p.16 / Chapter 3.2.1 --- First-Generation GPUs --- p.16 / Chapter 3.2.2 --- Second-Generation GPUs --- p.17 / Chapter 3.2.3 --- Third-Generation GPUs --- p.17 / Chapter 3.2.4 --- Fourth-Generation GPUs --- p.17 / Chapter 3.3 --- The Graphics Pipelining --- p.18 / Chapter 3.3.1 --- Standard Graphics Pipeline --- p.18 / Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.18 / Chapter 3.3.3 --- Fragment Processors for Scientific Computation --- p.21 / Chapter 3.4 --- GPU-CPU Analogy --- p.23 / Chapter 3.4.1 --- Memory Architecture --- p.23 / Chapter 3.4.2 --- Processing Model --- p.24 / Chapter 3.5 --- Limitation of GPU --- p.24 / Chapter 3.5.1 --- Limited Input and Output --- p.24 / Chapter 3.5.2 --- Slow Data Readback --- p.24 / Chapter 3.5.3 --- No Random Number Generator --- p.25 / Chapter 3.6 --- Summary --- p.25 / Chapter 4 --- Evolutionary Programming on GPU --- p.26 / Chapter 4.1 --- Introduction --- p.26 / Chapter 4.2 --- Evolutionary Programming --- p.26 / Chapter 4.3 --- Data Organization --- p.29 / Chapter 4.4 --- Fitness Evaluation --- p.31 / Chapter 4.4.1 --- Introduction --- p.31 / Chapter 4.4.2 --- Different Forms of Fitness Function --- p.32 / Chapter 4.4.3 --- Parallel Fitness Function Evaluation using GPU --- p.33 / Chapter 4.5 --- Mutation --- p.34 / Chapter 4.5.1 --- Introduction --- p.34 / Chapter 4.5.2 --- Self Adaptive Mutation Operators --- p.36 / Chapter 4.5.3 --- Mutation on GPU --- p.37 / Chapter 4.6 --- Selection for Replacement --- p.39 / Chapter 4.6.1 --- Introduction --- p.39 / Chapter 4.6.2 --- Classification of Selection Operator --- p.39 / Chapter 4.6.3 --- q -Tournament Selection --- p.40 / Chapter 4.6.4 --- Median Searching --- p.41 / Chapter 4.6.5 --- Minimizing Data Transfer --- p.43 / Chapter 4.7 --- Experimental Results --- p.44 / Chapter 4.7.1 --- Visualization --- p.48 / Chapter 4.8 --- Summary --- p.49 / Chapter 5 --- Genetic Algorithm on GPU --- p.56 / Chapter 5.1 --- Introduction --- p.56 / Chapter 5.2 --- Canonical Genetic Algorithm --- p.57 / Chapter 5.2.1 --- Parent Selection --- p.57 / Chapter 5.2.2 --- Crossover and Mutation --- p.62 / Chapter 5.2.3 --- Replacement --- p.63 / Chapter 5.3 --- Experiment Results --- p.64 / Chapter 5.4 --- Summary --- p.66 / Chapter 6 --- Multi-Objective Genetic Algorithm --- p.70 / Chapter 6.1 --- Introduction --- p.70 / Chapter 6.2 --- Definitions --- p.71 / Chapter 6.2.1 --- General MOP --- p.71 / Chapter 6.2.2 --- Decision Variables --- p.71 / Chapter 6.2.3 --- Constraints --- p.71 / Chapter 6.2.4 --- Feasible Region --- p.72 / Chapter 6.2.5 --- Optimal Solution --- p.72 / Chapter 6.2.6 --- Pareto Optimum --- p.73 / Chapter 6.2.7 --- Pareto Front --- p.73 / Chapter 6.3 --- Multi-Objective Genetic Algorithm --- p.75 / Chapter 6.3.1 --- Ranking --- p.76 / Chapter 6.3.2 --- Fitness Scaling --- p.77 / Chapter 6.3.3 --- Diversity Preservation --- p.77 / Chapter 6.4 --- A Niched and Elitism Multi-Objective Genetic Algorithm on GPU --- p.79 / Chapter 6.4.1 --- Objective Values Evaluation --- p.80 / Chapter 6.4.2 --- Pairwise Pareto Dominance and Pairwise Distance --- p.81 / Chapter 6.4.3 --- Fitness Assignment --- p.85 / Chapter 6.4.4 --- Embedded Archiving Replacement --- p.87 / Chapter 6.5 --- Experiment Result --- p.89 / Chapter 6.6 --- Summary --- p.90 / Chapter 7 --- Conclusion --- p.95 / Bibliography --- p.96
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_325321 |
| Date | January 2005 |
| Contributors | Fok, Ka Ling., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xii, 101 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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