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by Anthony Yiu-Cheung Tang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 145-152). / ACKNOWLEDGMENTS --- p.i / ABSTRACT --- p.ii / LIST OF FIGURES --- p.vii / LIST OF TABLES --- p.ix / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introducing evolutionary computation --- p.2 / Chapter 1.2 --- Adaptation and learning --- p.7 / Chapter 1.3 --- Comparing the efficency of evolutionary computation and sequential computation --- p.8 / Chapter 1.4 --- The place of evolutionary computation in computer science --- p.9 / Chapter 1.4.1 --- Mathematical foundation --- p.9 / Chapter 1.4.2 --- Scalability --- p.10 / Chapter 1.4.3 --- Parallelism --- p.11 / Chapter 1.5 --- Enhancing genetic search by local search --- p.11 / Chapter 1.6 --- Thesis Overview --- p.12 / Chapter 2. --- A REVIEW OF GENETIC ALGORITHMS --- p.14 / Chapter 2.1 --- Introduction --- p.14 / Chapter 2.2 --- The canonical genetic algorithm --- p.14 / Chapter 2.3 --- Optimal allocation of trials and schemata analysis --- p.17 / Chapter 2.4 --- Applications --- p.23 / Chapter 2.4.1 --- Function optimizations --- p.23 / Chapter 2.4.2 --- Machine Learning --- p.24 / Chapter 2.4.3 --- Combinatorial optimizations --- p.25 / Chapter 2.5 --- Criticisms --- p.25 / Chapter 2.5.1 --- Parameter settings --- p.25 / Chapter 2.5.2 --- Convergence and divergence --- p.26 / Chapter 2.5.3 --- Genetic algorithms for function optimizations --- p.27 / Chapter 2.5.4 --- The role of crossover and build blocks --- p.28 / Chapter 2.6 --- Future directions --- p.29 / Chapter 2.6.1 --- Is the schemata theorem wrong ？ --- p.29 / Chapter 2.6.2 --- Artificial life --- p.29 / Chapter 2.6.3 --- Parallel genetic algorithms --- p.31 / Chapter 2.6.4 --- Non-binary alphabets --- p.31 / Chapter 2.6.5 --- Investigations on problems that are hard for GA --- p.33 / Chapter 3. --- THE GENERAL STRUCTURE OF TOLKIEN --- p.34 / Chapter 3.1 --- Introduction --- p.34 / Chapter 3.2 --- Class Description --- p.39 / Chapter 3.2.1 --- Collection classes --- p.39 / Chapter 3.2.2 --- Vector classes --- p.39 / Chapter 3.2.3 --- GA-related classes --- p.40 / Chapter 3.2.4 --- Utility classes --- p.42 / Chapter 3.3 --- The TOLKIEN Genetic Algorithm --- p.43 / Chapter 3.3.1 --- Binary and Gray Code Representations --- p.44 / Chapter 3.3.2 --- Crossover Operators --- p.44 / Chapter 3.3.3 --- Haploids and Diploids --- p.47 / Chapter 3.3.4 --- Population --- p.50 / Chapter 3.3.5 --- Selection scheme --- p.50 / Chapter 3.3.6 --- Scaling scheme...: --- p.51 / Chapter 3.4 --- The TOLKIEN Classifier System --- p.52 / Chapter 3.4.1 --- Classifiers --- p.52 / Chapter 3.4.2 --- Messages and Message Lists --- p.53 / Chapter 3.4.3 --- Producing New Messages --- p.55 / Chapter 3.4.4 --- The Bucket Brigade Algorithm --- p.55 / Chapter 3.5 --- Where to obtain TOLKIEN --- p.56 / Chapter 4. --- ILLUSTRATING THE CAPABILITIES OF TOLKIEN --- p.57 / Chapter 4.1 --- de Jong's Test Bed : Function Optimization using GA --- p.57 / Chapter 4.2 --- Royal road function experiments --- p.63 / Chapter 4.2.1 --- RRMF --- p.64 / Chapter 4.2.2 --- RRJH --- p.65 / Chapter 4.2.3 --- Testing royal road functions using TOLKIEN --- p.68 / Chapter 4.2.4 --- Results --- p.71 / Chapter 4.2.5 --- Adding hillclimbing algorithm to solve royal road functions --- p.72 / Chapter 4.2.6 --- Discussions --- p.73 / Chapter 4.3 --- A classifier system to learn a multiplexer --- p.74 / Chapter 4.4 --- A classifier system maze traveller --- p.83 / Chapter 4.4.1 --- Framework of the Animat --- p.84 / Chapter 4.4.2 --- Constructing the maze navigation classifier system --- p.85 / Chapter 4.4.3 --- Results --- p.86 / Chapter 4.5 --- Future Enhancements on TOLKIEN --- p.88 / Chapter 4.6 --- Chapter Summary --- p.88 / Chapter 5. --- SOLVING TSP USING GENETIC ALGORITHMS --- p.89 / Chapter 5.1 --- Introduction --- p.89 / Chapter 5.2 --- Recombination operators for TSP --- p.91 / Chapter 5.2.1 --- PMX Crossover --- p.91 / Chapter 5.2.2 --- Order Crossover --- p.92 / Chapter 5.2.3 --- Edge Recombination operator --- p.93 / Chapter 5.3 --- Simulated Annealing --- p.95 / Chapter 5.4 --- Simulation Comparisons --- p.96 / Chapter 5.4.1 --- The Test Bed --- p.96 / Chapter 5.4.2 --- The Experimental Setup --- p.97 / Chapter 5.4.3 --- Results --- p.97 / Chapter 5.4.4 --- Discussions --- p.100 / Chapter 6. --- AN IMPROVED EDGE RECOMBINATION OPERATOR FOR TSP --- p.101 / Chapter 6.1 --- EDGENN ： a new edge recombination operator --- p.102 / Chapter 6.2 --- Experimental results --- p.104 / Chapter 6.2.1 --- Comparing EdgeNN and Edge-2 --- p.104 / Chapter 6.2.2 --- Comparing EdgeNN and Edge-3 --- p.106 / Chapter 6.3 --- Further improvement : a heuristic genetic algorithm using EdgeNN --- p.106 / Chapter 6.4 --- Discussion --- p.108 / Chapter 7. --- CONCLUSIONS --- p.111 / Chapter 7.1 --- Evaluation on TOLKIEN --- p.111 / Chapter 7.2 --- EdgeNN as a useful recombination operator for solving TSP --- p.112 / Chapter 7.3 --- Genetic algorithm and hillclimbing --- p.112 / EPILOGUE --- p.113 / APPENDIX : PROGRAM LISTINGS --- p.114 / Function optimizations --- p.114 / Maze Navigator --- p.122 / Multiplexer --- p.135 / Royal road functions --- p.141 / BIBLIOGRAPHY --- p.145 / INDEX --- p.153

by Lee, Ka-wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 61-67). / Chapter 1 --- Introduction --- p.6 / Chapter 1.1 --- Traveling Salesman Problem --- p.6 / Chapter 1.2 --- Genetic Algorithms --- p.8 / Chapter 1.3 --- Solving TSP using Genetic Algorithms --- p.10 / Chapter 1.4 --- Outline of Work --- p.12 / Chapter Part I --- Algorithm Development --- p.14 / Chapter 2 --- A Local DP Crossover Operator 一 LDPX --- p.15 / Chapter 2.1 --- Review of DP for Solving TSP --- p.15 / Chapter 2.2 --- On the Original LDPX --- p.18 / Chapter 2.2.1 --- Gene Representation --- p.18 / Chapter 2.2.2 --- The Original Crossover Procedure --- p.19 / Chapter 2.3 --- Analysis --- p.21 / Chapter 2.3.1 --- Ring TSP --- p.21 / Chapter 2.3.2 --- Computational Results of Solving Ring TSP and Other TSP using LDPX --- p.22 / Chapter 2.4 --- Augmentation of the Gene Set Representation --- p.24 / Chapter 2.5 --- Enhancement of Crossover Procedure --- p.25 / Chapter 2.6 --- Computational Comparison of the new proposed LDPX with the orig- inal LDPX --- p.26 / Chapter 2.7 --- SPIR ´ؤ An Operator for Single Parent Improved Reproduction --- p.26 / Chapter 3 --- A New TSP Solver --- p.29 / Chapter 4 --- Performance Analysis of the TSP Solver --- p.33 / Chapter 4.1 --- Computational results --- p.34 / Chapter 4.2 --- "Comparison between SPIR/LDPX, PMX and ER" --- p.35 / Chapter 4.3 --- Convergence Test of SPIR/LDPX --- p.37 / Chapter Part II --- Application --- p.43 / Chapter 5 --- Flowshop Scheduling Problem --- p.44 / Chapter 5.1 --- Brief Review of the Flowshop Scheduling Problem --- p.44 / Chapter 5.2 --- Flowshop Scheduling with travel times between machines --- p.45 / Chapter 6 --- A New Approach to Solve FSTTBM --- p.47 / Chapter 7 --- Computational Results of the New Algorithm for CPFSTTBM --- p.53 / Chapter 7.1 --- Comparison with Global Optimum --- p.54 / Chapter 7.2 --- The Algorithm of SPIRIT --- p.55 / Chapter 7.3 --- Comparison with SPIRIT --- p.57 / Chapter 8 --- Conclusion --- p.59 / Bibliography --- p.61 / Chapter A --- Random CPFSTTBM problem Generation Algorithm --- p.68

Ho Kin Chuen Matthew. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 153-157). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.15 / Chapter 1.1 --- The U-line Balancing Problem --- p.15 / Chapter 1.2 --- Configuration of an U-line --- p.17 / Chapter 1.3 --- Feasible subsets and sequences --- p.19 / Chapter 1.4 --- Assignment of tasks to stations --- p.21 / Chapter 1.5 --- Costs --- p.22 / Chapter 1.6 --- Formulation of The U-line Balancing Problem --- p.23 / Chapter 1.7 --- Design of computational study --- p.25 / Chapter 1.7.1 --- Input parameters --- p.25 / Chapter 1.7.2 --- Output variables --- p.26 / Chapter 1.7.3 --- Problems solved --- p.27 / Chapter 1.7.3.1 --- Problem Set One --- p.28 / Chapter 1.7.3.2 --- Problem Set Two --- p.28 / Chapter 1.7.3.3 --- Problem Set Three --- p.29 / Chapter 1.7.3.4 --- Problem Set Four --- p.29 / Chapter 1.8 --- Contributions --- p.29 / Chapter 1.9 --- Organization of thesis --- p.30 / Chapter 2 --- Literature Review --- p.31 / Chapter 2.1 --- Introduction --- p.31 / Chapter 2.2 --- The Straight-line Balancing Problem --- p.32 / Chapter 2.2.1 --- Single-model Assembly Line Balancing with deterministic task time (SMD) --- p.34 / Chapter 2.2.2 --- Single-model Assembly Line Balancing with stochastic task times (SMS) --- p.36 / Chapter 2.2.3 --- Multi/Mixed-model Assemble Line Balancing with deterministic task times (MMD) --- p.37 / Chapter 2.2.4 --- Multi/Mixed-model Assembly Line Balancing with stochastic task times (MMS) --- p.38 / Chapter 2.3 --- The U-line Balancing Problem --- p.39 / Chapter 2.4 --- Conclusions --- p.45 / Chapter 3 --- Heuristic Methods --- p.47 / Chapter 3.1 --- Introduction --- p.47 / Chapter 3.2 --- Single-pass heuristic methods --- p.47 / Chapter 3.3 --- Computational results --- p.50 / Chapter 3.3.1 --- Problem Set One --- p.50 / Chapter 3.3.2 --- Problem Set Two --- p.52 / Chapter 3.3.3 --- Problem Set Three --- p.54 / Chapter 3.3.4 --- Problem Set Four --- p.55 / Chapter 3.4 --- Discussions --- p.57 / Chapter 3.5 --- Conclusions --- p.59 / Chapter 4 --- Genetic Algorithm --- p.60 / Chapter 4.1 --- Introduction --- p.60 / Chapter 4.2 --- Application of GA to The Straight-line Balancing Problem --- p.61 / Chapter 4.3 --- Application of GA to The U-line Balancing Problem --- p.62 / Chapter 4.3.1 --- Coding scheme --- p.63 / Chapter 4.3.2 --- Initial population --- p.64 / Chapter 4.3.3 --- Fitness function --- p.65 / Chapter 4.3.4 --- Selection scheme --- p.66 / Chapter 4.3.5 --- Reproduction --- p.67 / Chapter 4.3.6 --- Replacement scheme --- p.68 / Chapter 4.3.7 --- Elitism --- p.68 / Chapter 4.3.8 --- Termination criteria --- p.68 / Chapter 4.4 --- Repair method --- p.69 / Chapter 4.5 --- Crossover operators --- p.71 / Chapter 4.5.1 --- Sequence and configuration infeasible crossover operators --- p.72 / Chapter 4.5.1.1 --- Partially Mapped Crossover (PMX) --- p.72 / Chapter 4.5.1.2 --- Order Crossover #1 (ORD#l) --- p.74 / Chapter 4.5.1.3 --- Order Crossover #2 (ORD#2) --- p.74 / Chapter 4.5.1.4 --- Position Based Crossover (POS) --- p.75 / Chapter 4.5.1.5 --- Cycle Crossover (CYC) --- p.76 / Chapter 4.5.1.6 --- Edge Recombination Crossover (EDG) --- p.77 / Chapter 4.5.1.7 --- Enhanced Edge Recombination Crossover (EEDG) --- p.80 / Chapter 4.5.1.8 --- Uniform-order Based Crossover (UOX) --- p.81 / Chapter 4.5.2 --- Sequence feasible but configuration infeasible crossover operators --- p.82 / Chapter 4.5.2.1 --- One-point Crossover (1PX) --- p.82 / Chapter 4.5.2.2 --- Two-point Crossover (2PX) --- p.84 / Chapter 4.5.2.3 --- Uniform Crossover (UX) --- p.85 / Chapter 4.6 --- Mutation operators --- p.86 / Chapter 4.6.1 --- Sequence infeasible mutation operators --- p.87 / Chapter 4.6.1.1 --- Inversion (INV) --- p.87 / Chapter 4.6.1.2 --- Insertion (INS) --- p.87 / Chapter 4.6.1.3 --- Displacement (DIS) --- p.88 / Chapter 4.6.1.4 --- Reciprocal Exchange (RE) --- p.88 / Chapter 4.6.2 --- Sequence and configuration feasible mutation operators --- p.89 / Chapter 4.6.2.1 --- Scramble Mutation (SCR) --- p.89 / Chapter 4.6.2.2 --- Feasible Insertion (FINS) --- p.90 / Chapter 4.7 --- Computational study --- p.91 / Chapter 4.7.1 --- Comparison of crossover operators --- p.91 / Chapter 4.7.2 --- Comparison of mutation operators --- p.95 / Chapter 4.7.2.1 --- Order crossover#2 and mutation operators --- p.95 / Chapter 4.7.2.2 --- Position based crossover and mutation operators --- p.97 / Chapter 4.7.3 --- Parameters setting --- p.99 / Chapter 4.7.4 --- Computational results --- p.104 / Chapter 4.7.5 --- Comparative results --- p.105 / Chapter 4.7.5.1 --- Problem Set One --- p.105 / Chapter 4.7.5.2 --- Problem Set Two --- p.105 / Chapter 4.7.5.3 --- Problem Set Three --- p.107 / Chapter 4.7.5.4 --- Problem Set Four --- p.107 / Chapter 4.8 --- Conclusions --- p.109 / Chapter 5 --- Dynamic Programming and Lower Bounds --- p.110 / Chapter 5.1 --- Dynamic Programming (DP) --- p.110 / Chapter 5.1.1 --- Introduction --- p.110 / Chapter 5.1.2 --- Modified Dynamic Programming algorithm --- p.112 / Chapter 5.1.3 --- Comparison between optimal solution and heuristics --- p.120 / Chapter 5.1.4 --- Comparison between optimal solution and the GA --- p.123 / Chapter 5.2 --- Lower Bounds --- p.123 / Chapter 5.2.1 --- Introduction --- p.123 / Chapter 5.2.2 --- The U-line Balancing Problem and The Bin Packing Problem --- p.127 / Chapter 5.2.3 --- Martello and Toth's lower bounds for The BPP --- p.128 / Chapter 5.2.3.1 --- Bound L1 --- p.128 / Chapter 5.2.3.2 --- Bound L2 --- p.128 / Chapter 5.2.3.3 --- Dominances and reductions --- p.129 / Chapter 5.2.3.3.1 --- Dominance criterion --- p.129 / Chapter 5.2.3.3.2 --- Reduction procedure --- p.130 / Chapter 5.2.3.4 --- Lower Bound LR --- p.131 / Chapter 5.2.4 --- Chen and Srivastava's lower bounds for The BPP --- p.131 / Chapter 5.2.4.1 --- A unified lower bound --- p.132 / Chapter 5.2.4.2 --- Improving Lm --- p.133 / Chapter 5.2.4.3 --- "Computing a lower bound on N(1/4,1]" --- p.134 / Chapter 5.2.5 --- Lower bounds for The U-line Balancing Problem --- p.137 / Chapter 5.2.5.1 --- Lower bounds on number of stations required --- p.137 / Chapter 5.2.5.2 --- Lower bounds on total cost --- p.139 / Chapter 5.2.6 --- Computational results --- p.140 / Chapter 5.2.6.1 --- Results for different Problem Sets --- p.140 / Chapter 5.2.6.2 --- Comparison between lower bounds and optimal solutions --- p.143 / Chapter 5.2.6.3 --- Comparison between lower bounds and heuristics --- p.145 / Chapter 5.2.6.4 --- Comparison between lower bounds and GA --- p.147 / Chapter 5.3 --- Conclusions --- p.149 / Chapter 6 --- Conclusions --- p.150 / Chapter 6.1 --- Summary of achievements --- p.150 / Chapter 6.2 --- Future works --- p.151

Land use optimization is a multifaceted process that entails complex decision-making which involves the selection of activities, the percentages to allocate, and where to allocate. It will also add a whole extra class of variables to the problem when combined with the inevitable consideration of spatial optimization. The related applications by linear programming (LP), "Pareto Front Optimal" based methods, heuristics methods and integration of GIS etc. for spatial multi-objective land use optimization are reviewed and analyzed on their advantages and disadvantages in this thesis. Accordingly, due to the nonlinearity and the complexity caused by the multiple objectives and increasing variables during the optimization process, the efficiency and effect would be the issues to be considered. The need for effective and efficient models for land use optimization is evident from the above discussion as the core content. In order to comprehensively fulfill all the requirements, the understanding of the sustainability of land use is translated into eight objectives to form the Multi-objective Optimization of Land Use (MOLU) model. Furthermore, an efficient model named Boundary based Fast Genetic Algorithm (BFGA) using goal programming is employed in the multi-objective optimization in Tongzhou Newtown. This algorithm is especially efficient for land use optimization problems derived from its special boundary based operators. Furthermore, considering the characteristics of planning support process and these two models mentioned above, the interactive spatial land use optimization prototype with a friendly interface and a simplified 3D visualization module could be established, thus yielding good effects and potential to support the planning process in the study area. Finally, in light of the study results and limitations, some directions are also provided for future research. / Land use optimization, a kind of resource allocation, can be defined as the process of allocating different land use categories (e.g., residential, commercial, and industrial, etc.) to specific units of area within a region. As one of the most popular words nowadays, sustainable development can be viewed as a process of change in which the exploitation of resources, the direction of investment, the orientation of technological development and institutional change are all harmonized. Sustainability is, hence, an important and imminent societal goal for land use planning. Land use optimization involves the active planning of land for future use by people to provide for their needs. In this thesis, the central goal is to develop a sustainable land use optimization prototype to enrich the field of planning support with regard to sustainability. / Cao, Kai. / Source: Dissertation Abstracts International, Volume: 72-04, Section: A, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 132-139). / 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. / Abstract also in Chinese.

While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. This individual model approximates the output value only for this input vector. However, deciding on the appropriate distance measure, number of nearest neighbours and a minimum set of important features/variables is a challenge and is usually based on prior knowledge or exhaustive trial and test experiments.Proposed algorithm - This thesis proposes a Genetic Algorithm (GA) method for optimising these three factors using a transductive approach. This novel approach called Individualised Modeling using Transductive Inference and Genetic Algorithms (IMTIGA) is tested on several datasets from UCI repository for classification task and real world scenario for pest establishment prognosis and results show that it outperforms conventional, inductive approaches of global and local modelling.

This record of study summarizes the work accomplished during the internship at the Energy Systems Laboratory of the Texas Engineering Experiment Station. The internship project was to develop a tool to optimize the building parameters so that the overall building energy cost is minimized. A metaheuristic: genetic algorithm was identified as the solution algorithm and was implemented in the problem under study. Through two case studies, the impacts of the three genetic algorithm parameters, namely population size, crossover and mutation rates, on the algorithm's overall performance are also studied through statistical tests. Through these statistical tests, the optimum combination of above the mentioned parameters is also identified and applied. Finally, a performance analysis based on the case studies show that the tool achieved satisfactory results.

The field of automated timetabling and scheduling meeting all the requirementsthat we call constraints is always difficult task and already proved as NPComplete. The idea behind my research is to implement Genetic Algorithm ongeneral scheduling problem under predefined constraints and check the validityof results, and then I will explain the possible usage of other approaches likeexpert systems, direct heuristics, network flows, simulated annealing and someother approaches. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems. The program written in C++ and analysisis done with using various tools explained in details later.

This record of study summarizes the work accomplished during the internship at the Energy Systems Laboratory of the Texas Engineering Experiment Station. The internship project was to develop a tool to optimize the building parameters so that the overall building energy cost is minimized. A metaheuristic: genetic algorithm was identified as the solution algorithm and was implemented in the problem under study. Through two case studies, the impacts of the three genetic algorithm parameters, namely population size, crossover and mutation rates, on the algorithm's overall performance are also studied through statistical tests. Through these statistical tests, the optimum combination of above the mentioned parameters is also identified and applied. Finally, a performance analysis based on the case studies show that the tool achieved satisfactory results.

Thesis (M. Phil.)--University of Hong Kong, 2009. / Includes bibliographical references. Also available online.