Tse, Sui Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 95-104). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.1.1 --- Basic Concepts and Definitions --- p.2 / Chapter 1.2 --- Evolutionary Algorithms --- p.5 / Chapter 1.2.1 --- Chromosome Encoding --- p.6 / Chapter 1.2.2 --- Selection --- p.7 / Chapter 1.2.3 --- Crossover --- p.9 / Chapter 1.2.4 --- Mutation --- p.10 / Chapter 1.2.5 --- Elitism --- p.11 / Chapter 1.2.6 --- Niching --- p.11 / Chapter 1.2.7 --- Population Manipulation --- p.13 / Chapter 1.2.8 --- Building Blocks --- p.13 / Chapter 1.2.9 --- Termination Conditions --- p.14 / Chapter 1.2.10 --- Co-evolution --- p.14 / Chapter 1.3 --- Local Search --- p.15 / Chapter 1.4 --- Memetic Algorithms --- p.16 / Chapter 1.5 --- Objective --- p.17 / Chapter 1.6 --- Summary --- p.17 / Chapter 2 --- Background --- p.18 / Chapter 2.1 --- Multiple Drugs Tumor Chemotherapy --- p.18 / Chapter 2.2 --- Bioinformatics --- p.22 / Chapter 2.2.1 --- Basics of Bioinformatics --- p.24 / Chapter 2.2.2 --- Applications on Biomedical Systems --- p.26 / Chapter 3 --- A New Drug Administration Dynamic Model --- p.29 / Chapter 3.1 --- Three Drugs Mathematical Model --- p.31 / Chapter 3.1.1 --- Rate of Change of Different Subpopulations --- p.32 / Chapter 3.1.2 --- Rate of Change of Different Drug Concen- trations --- p.35 / Chapter 3.1.3 --- Toxicity Effects --- p.35 / Chapter 3.1.4 --- Summary --- p.36 / Chapter 4 --- Memetic Algorithm - Iterative Dynamic Program- ming (MA-IDP) --- p.38 / Chapter 4.1 --- Problem Formulation: Optimal Control Problem (OCP) for Mutlidrug Optimization --- p.38 / Chapter 4.2 --- Proposed Memetic Optimization Algorithm --- p.40 / Chapter 4.2.1 --- Iterative Dynamic Programming (IDP) . . --- p.40 / Chapter 4.2.2 --- Adaptive Elitist-population-based Genetic Algorithm (AEGA) --- p.44 / Chapter 4.2.3 --- Memetic Algorithm 一 Iterative Dynamic Programming (MA-IDP) --- p.50 / Chapter 4.3 --- Summary --- p.56 / Chapter 5 --- MA-IDP: Experiments and Results --- p.57 / Chapter 5.1 --- Experiment Settings --- p.57 / Chapter 5.2 --- Optimization Results --- p.61 / Chapter 5.3 --- Extension to Other Mutlidrug Scheduling Model . --- p.62 / Chapter 5.4 --- Summary --- p.65 / Chapter 6 --- DNA Sequencing by Hybridization (SBH) --- p.66 / Chapter 6.1 --- Problem Formulation: Reconstructing a DNA Sequence from Hybridization Data --- p.70 / Chapter 6.2 --- Proposed Memetic Optimization Algorithm --- p.71 / Chapter 6.2.1 --- Chromosome Encoding --- p.71 / Chapter 6.2.2 --- Fitness Function --- p.73 / Chapter 6.2.3 --- Crossover --- p.74 / Chapter 6.2.4 --- Hill Climbing Local Search for Sequencing by Hybridization --- p.76 / Chapter 6.2.5 --- Elitism and Diversity --- p.79 / Chapter 6.2.6 --- Outline of Algorithm: MA-HC-SBH --- p.81 / Chapter 6.3 --- Summary --- p.82 / Chapter 7 --- DNA Sequencing by Hybridization (SBH): Experiments and Results --- p.83 / Chapter 7.1 --- Experiment Settings --- p.83 / Chapter 7.2 --- Experiment Results --- p.85 / Chapter 7.3 --- Summary --- p.89 / Chapter 8 --- Conclusion --- p.90 / Chapter 8.1 --- Multiple Drugs Cancer Chemotherapy Schedule Optimization --- p.90 / Chapter 8.2 --- Use of the MA-IDP --- p.91 / Chapter 8.3 --- DNA Sequencing by Hybridization (SBH) --- p.92 / Chapter 8.4 --- Use of the MA-HC-SBH --- p.92 / Chapter 8.5 --- Future Work --- p.93 / Chapter 8.6 --- Item Learned --- p.93 / Chapter 8.7 --- Papers Published --- p.94 / Bibliography --- p.95