Spelling suggestions: "subject:"[een] GENETIC ALGORITHMS"" "subject:"[enn] GENETIC ALGORITHMS""
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Accelerated strategies of evolutionary algorithms for optimization problem and their applications. / CUHK electronic theses & dissertations collection / Digital dissertation consortiumJanuary 2003 (has links)
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.
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Application of genetic algorithms to group technology.January 1996 (has links)
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
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On the design of multiplier-less perfect reconstruction filter banks using genetic algorithm and sum-of-powers-of-two representation /Liu, Wei, January 2000 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references.
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Analysis and synthesis of positive systems and related gene network modelsLi, Ping, 李平 January 2011 (has links)
The Best PhD Thesis in the Faculties of Dentistry, Engineering, Medicine and Science (University of Hong Kong), Li Ka Shing Prize,2010-11 / published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy
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On the design of multiplier-less perfect reconstruction filter banks using genetic algorithm and sum-of-powers-of-two representationLiu, Wei, 劉偉 January 2000 (has links)
published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy
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Studies on the theory and design space of memetic algorithmsKrasnogor, Natalio January 2002 (has links)
No description available.
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An evolutionary computing approach to motor learning with an application to robot manipulatorsSullivan, J. Charles W. January 2001 (has links)
No description available.
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Evolving artificial neural network controllers for robots using species-based methodsHutt, Benjamin David January 2002 (has links)
No description available.
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Using genetic algorithms to optimize functions of microbial ecosystems /Vandecasteele, Frederik P.J. January 1900 (has links)
Thesis (Ph. D., Biological and Agricultural Engineering)--University of Idaho, July 2006. / Major professor: Thomas F. Hess. Includes bibliographical references. Also available online (PDF file) by subscription or by purchasing the individual file.
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Two-stage hyhrid flowshop scheduling in a metalworking company using genetic algorithmLuo, Hao, January 2009 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 127-135). Also available in print.
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