Kiel, University, Diss., 2001.

Zugl.: Düsseldorf, Universiẗat, Diss., 1999.

Techn. Hochsch., Diss., 2000--Aachen.

No description available.

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

Nežinomo ploto dengimas yra aktuali ir paplitusi problema. NPD sprendimas realiuose robotuose susiduria su daviklių ir mechanizmų netikslumu. Atliktame darbe yra pateiktas „Bouncing“ NPD algoritmo sprendimas robotui, turinčiam mažo tikslumo daviklius ir neprecizinius valdiklius. Taip pat atliktas darbas parodė sudėtingus roboto kūrimo aspektus ir galimus sprendimus. Sukurtas robotas dėl pigumo ir nesudėtingos realizacijos gali būti naudojamas kaip platforma kitokių algoritmų tyrimui. / The problem of unknown area coverage with mobile robots has received considerable attention over the past years. This problem is a common challenge in many applications, including automatic lawn mowing and vacuum cleaning. However, most of the approaches find difficult to implement in real life because of problems of environment data reading. In this paper we consider the problem of robust area covering algorithm implementation in mobile robot. The chosen approach is based on simple and robust algorithm for uncertain environment and simple robot platform. The results showed robustness, reliability of chosen method of control. The constructed robot has shown simplicity, cheapness of creation and possibility for different algorithm testing. The significance of this paper lies in the practical solution for robust mobile robot area coverage, suitable for noisy environment and low precisions robot sensors.

No description available.

Thesis(B.Sc.(Hons. ))--University of Adelaide, dept. of Mathematics, 1968.