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<p> This thesis defends the use of genetic algorithms (GA) to solve the maximum number of repetitions in a binary string. Repetitions in strings have significant uses in many different fields, whether it is data-mining, pattern-matching, data compression or computational biology 14]. Main extended the definition of repetition, he realized that in some cases output could be reduced because of overlapping repetitions, that are simply rotations of one another [10]. As a result, he designed the notion of a run to capture the maximal leftmost repetition that is extended to the right as much as possible. Franek and Smyth independently computed the same number of maximum repetition for strings of length five to 35 using an exhaustive search method. Values greater than 35 were not computed because of the exponential increase in time required. Using GAs we are able to generate string with very large, if not the maximum, number of runs for any string length. The ability to generate strings with large runs is an advantage for learning more about the characteristics of these strings. </p> / Thesis / Master of Science (MSc)

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Yes / Personalized medicine through Pharmacogenomics: choosing the right drug, and the right dose, for the right patients based on patient’s genetic makeup-is gradually being realised in Western countries. Yet, the practice of pharmacogenomics in Asian countries lags behind that of the West, but the medical needs for pharmacogenomics are expected to surge as better patient care is demanded in Asia. As next-generation sequencing technology advances quickly, previous technical challenges for performing pharmacogenomic studies or practices in Asia have been mostly resolved. What is lacking in Asia is an effective model of community-wide pharmacogenomics. On the delivery front, pharmacists, the drug and dosing professionals, can potentially be the main healthcare providers for pharmacogenomic services in Asia. The first large “Genomics for Precision Drug Therapy in the Community Pharmacy” in Canada, which is close to its completion, has successfully identified community pharmacists as key contact professionals for smooth facilitation and implementation of pharmacogenomics for personalized medication. It is anticipated that Asian pharmacists, with appropriate training, can have the capacity to provide expert pharmacogenomic supports for both physicians and patients in Asia. / The School of Biomedical Sciences Start-up Fund, the Chinese University of Hong Kong, the General Research Fund (#17114814; #17121616), the Theme-based Research Scheme (T12-401/13-R), Research Grant Council, Hong Kong, as well as the Hong Kong Cancer Fund, Hong Kong.

Published Article / People have tried different ways to make machines intelligent. One option is to use a simulated neural net as a platform for Genetic Algorithms. Neural nets are a combination of neurons in a certain pattern. Neurons in a neural net system are a simulation of neurons in an organism's brain. Genetic Algorithms represent an emulation of evolution in nature. The question arose as to why write a program to simulate neurons if a program can execute the functions a combination of neurons would generate. For this reason a virtual robot indicated in Figure 1 was made "intelligent" by developing a process where the robot creates a program for itself. Although Genetic Algorithms might have been used in the past to generate a program, a new method called Single-Chromosome-Evolution-Algorithms (SCEA) was introduced and compared to Genetic Algorithms operation. Instructions in the program were changed by using either Genetic Algorithms or alternatively with SCEA where only one simulation was needed per generation to be tested by the fitness of the system.

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