In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of optimizing nonlinear crop models and describe application requirements and implementation of the technique. GAs were found to converge quickly to near-optimal solutions. Second, we analyze the relationship between GA control parameters (population size, crossover rate, and mutation rate) and performance. We identify a combination of population, mutation, and crossover which searched the fitness landscape efficiently. The results suggest that smaller populations are able to provide better performance at relatively low mutation rates. More stable outcomes were generated using low mutation rates. Without crossover the quality of solutions were generally impaired, and the search process was lengthened. Aside from crossover rate zero, no other crossover rates significantly differed. The behaviors observed for best, online, offline, and average performances were sensitive to the combined influences control parameters. Interaction among control parameters was strongly indicated. Finally, several adaptive penalty techniques are presented for handling constraints in GAs, and their effectiveness is demonstrated. The constant penalty function suffered from sensitivity to settings of penalty coefficients, and was not successful in satisfying constraints. The adaptive penalty functions utilizes violation distance based metrics and search time based scaling using generation or trials number, and fitness values to penalize infeasible solutions, as the distance from the feasible region or number of generations increases so does the penalty. They were quite successful in providing solutions with minimal effort. They adapt the penalty as the search continues, encouraging feasible solutions to emerge over the time. Adaptive approaches presented here are flexible, efficient, and robust to parameter settings. / Graduation date: 1997

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references.

Thesis (M.S.)--California Polytechnic State University, 2010. / Mode of access: Internet. Title from PDF title page; viewed on March 19, 2010. Major professor: Franz Kurfess, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Computer Science." "March 2010." Includes bibliographical references (p. 150-154).

The Best PhD Thesis in the Faculties of Dentistry, Engineering, Medicine and Science (University of Hong Kong), Li Ka Shing Prize,2010-11 / published_or_final_version / Mechanical Engineering / Doctoral / Doctor of Philosophy

Our understanding of the functions played by RNA molecules is expanded with the understanding of RNA structures. Except for primary structure, RNA molecules present pairings within a sequence, which is called RNA secondary structure. Since its discovery, RNA secondary structure has drawn considerable attention because it is widely appeared. Many programs for RNA secondary structure prediction have been developed, including [4, 20, 38, 39, 46]. Based on our knowledge, however, there is a family of RNA secondary structure which can not be covered by any of these algorithms. And even without considering this family, none of programs can cover all other structures in Rfam data-set. These structures are found to be important in many biological processes, for example, chromosome maintenance, RNA processing, protein biosynthesis. And efficient structure prediction can give direction for experimental investigations. Here, we present a general algorithm with a new grammar: Vertical Tree Grammar (VTG) which has stochastic context-free grammar architecture for RNA secondary structure prediction. VTG significantly expands the class of structures that can be handled, including all structures that can be covered by other paper, and all structures in Rfam data-set. Our algorithm runs in O(n^6) time, and it's precision is reasonable high, with average sensitivity and specificity over 70%. / published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy

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