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Thesis (M.S.)--Ohio University, March, 1985. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, November, 1992. / Title from PDF t.p.

Thesis (Honors)--Liberty University Honors Program, 2006. / Includes bibliographical references. Also available through Liberty University's Digital Commons.

published_or_final_version / Mathematics / Master / Master of Philosophy

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

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