This paper describes an intuitive way of defining geometry design variables for solving structural topology optimization problems using a genetic algorithm (GA). The geometry representation scheme works by defining a skeleton that represents the underlying topology/connectivity of the continuum structure. As the effectiveness of any GA is highly dependent on the chromosome encoding of the design variables, the encoding used here is a directed graph which reflects this underlying topology so that the genetic crossover and mutation operators of the GA can recombine and preserve any desirable geometric characteristics through succeeding generations of the evolutionary process. The overall optimization procedure is tested by solving a simulated topology optimization problem in which a 'target' geometry is pre-defined with the aim of having the design solutions converge towards this target shape. The procedure is also applied to design a straight-line compliant mechanism : a large displacement flexural structure that generates a vertical straight line path at some point when given a horizontal straight line input displacement at another point. / Singapore-MIT Alliance (SMA)

Inferring phylogeny is a difficult computational problem. Heuristics are necessary to minimize the time spent evaluating non optimal trees. In paper I, we developed an approach for heuristic searching, using a genetic algorithm. Genetic algorithms mimic the natural selections ability to solve complex problems. The algorithm can reduce the time required for weighted maximum parsimony phylogenetic inference using protein sequences, especially for data sets involving large number of taxa. Evaluating and comparing the ability of phylogenetic methods to infer the correct topology is complex. In paper II, we developed software that determines the minimum subtree prune and regraft (SPR) distance between binary trees to ease the process. The minimum SPR distance can be used to measure the incongruence between trees inferred using different methods. Given a known topology the methods could be evaluated on their ability to infer the correct phylogeny given specific data. The minimum SPR software the intermediate trees that separate two binary trees. In paper III we developed software that given a set of incongruent trees determines the median SPR consensus tree i.e. the tree that explains the trees with a minimum of SPR operations. We investigated the median SPR consensus tree and its possible interpretation as a species tree given a set of gene trees. We used a set of α-proteobacteria gene trees to test the ability of the algorithm to infer a species tree and compared it to previous studies. The results show that the algorithm can successfully reconstruct a species tree. Expressed sequence tag (EST) data is important in determining intron-exon boundaries, single nucleotide polymorphism and the coding sequence of genes. In paper IV we aligned ESTs to the genome to evaluate the quality of EST data. The results show that many ESTs are contaminated by vector sequences and low quality regions. The reliability of EST data is largely determined by the clustering of the ESTs and the association of the clusters to the correct portion of genome. We investigate the performance of EST clustering using the genome as template compared to previously existing methods using pair-wise alignments. The results show that using the genome as guidance improves the resulting EST clusters in respect to the extent ESTs originating from the same transcriptional unit are separated into disjunct clusters.

Traveling salesman problem (TSP) is a well-known NP-hard problem which can not be solved within a polynomial bounded computation time. However, genetic algorithm (GA) is a familiar heuristic algorithm to obtain near-optimal solutions within reasonable time for TSPs. In TSPs, the geometric properties are problem specific knowledge can be used to enhance GAs. Some tour segments (edges) of TSPs are fine while some maybe too long to appear in a short tour. Therefore, this information can help GAs to pay more attention to fine tour segments and without considering long tour segments as often. Consequently, we propose a new algorithm, called intelligent-OPT hybrid genetic algorithm (IOHGA), to exploit local optimal tour segments and enhance the searching process in order to reduce the execution time and improve the quality of the offspring. The local optimal tour segments are assigned higher priorities for the selection of tour segments to be appeared in a short tour. By this way, tour segments of a TSP are divided into two separate sets. One is a candidate set which contains the candidate fine tour segments and the other is a non-candidate set which contains non-candidate fine tour segments. According to the priorities of tour segments, we devise two genetic operators, the skewed production (SP) and the fine subtour crossover (FSC). Besides, we combine the traditional GA with 2-OPT local search algorithm but with some modifications. The modified 2-OPT is named the intelligent OPT (IOPT). Simulation study was conducted to evaluate the performance of the IOHGA. The experimental results indicate that generally the IOHGA could obtain near-optimal solutions with less time and higher accuracy than the hybrid genetic algorithm with simulated annealing algorithm and the genetic algorithm using the gene expression algorithm. Thus, the IOHGA is an effective algorithm for solving TSPs. If the case is not focused on the optimal solution, the IOHGA can provide good near-optimal solutions rapidly. Therefore, the IOHGA could be incorporated with some clustering algorithm and applied to mobile agent planning problems (MAP) in a real-time environment.

For the mass demands of personalized security services, such as tracking, supervision, and emergent rescue, the location technologies of mobile communication have drawn much attention of the governments, academia, and industries around the world. However, existing location methods cannot satisfy the requirements of low cost and high accuracy. We hypothesized that a new mobile location algorithm based on the current GSM system will effectively improve user satisfaction. In this study, a prototype system will be developed, implemented, and experimented by integrating the useful information such as the geometry of the cell layout, and the related mobile positioning technologies. The intersection of the regions formed by the communication space of the base stations will be explored. Furthermore, the density-based clustering algorithm (DCA) and GA-based algorithm will be designed to analyze the intersection region and estimate the most possible location of a mobile phone. Simulation results show that the location error of the GA-based is less than 0.075 km for 67% of the time, and less than 0.15 km for 95% of the time. The results of the experiments satisfy the location accuracy demand of E-911.

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In the Canadian universal healthcare system, public access to care is not limited by monetary or social economic factors. Rather, waiting time is the dominant factor limiting public access to healthcare. Excessive waiting lowers quality of life while waiting, and worsening of condition during the delay, which could lower the effectiveness of the planned operation. Excessive waiting has also been shown to carry economic cost. At the core of the wait time problem is a resource scheduling and management issue. The scheduling of medical procedures is a complex and difficult task. The goal of research in this thesis is to develop the foundation models and algorithms for a resource optimization system. Such a system will help healthcare administrators intelligently schedule procedures to optimize resource utilization, identify bottlenecks and reduce patient wait times. This thesis develops a novel framework, the MPSP model, to model medical procedures. The MPSP model is designed to be general and versatile to model a variety of different procedures. The specific procedure modeled in detail in this thesis is the haemodialysis procedure. Solving the MPSP model exactly to obtain guaranteed optimal solutions is computationally expensive and not practical for real-time scheduling. A fast, high quality evolutionary heuristic, gMASH, is developed to quickly solve large problems. The MPSP model and the gMASH heuristic form a foundation for an intelligent medical procedures scheduling and optimization system.

This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discussed. To improve the performance of the fuzzy controller, a dynamic fuzzy controller is proposed. By switching the fuzzy controller elements in relation to the step response, both transient and stationary performances are enhanced. To endow the fuzzy controller with intelligence of generalization, flexibility and adaptivity, self-learning techniques are introduced to obtain fuzzy controller parameters. With the inclusion of supervision and concern for conventional control criteria, the parameters of the fuzzy inference system are tuned by a backward propagation algorithm or their optimal values are located by means of a genetic algorithm. In simulations, the neuro-fuzzy tension controller exhibits the real-time applicability, while the genetic fuzzy tension controller reveals an outstanding global optimization ability.

In the Canadian universal healthcare system, public access to care is not limited by monetary or social economic factors. Rather, waiting time is the dominant factor limiting public access to healthcare. Excessive waiting lowers quality of life while waiting, and worsening of condition during the delay, which could lower the effectiveness of the planned operation. Excessive waiting has also been shown to carry economic cost. At the core of the wait time problem is a resource scheduling and management issue. The scheduling of medical procedures is a complex and difficult task. The goal of research in this thesis is to develop the foundation models and algorithms for a resource optimization system. Such a system will help healthcare administrators intelligently schedule procedures to optimize resource utilization, identify bottlenecks and reduce patient wait times. This thesis develops a novel framework, the MPSP model, to model medical procedures. The MPSP model is designed to be general and versatile to model a variety of different procedures. The specific procedure modeled in detail in this thesis is the haemodialysis procedure. Solving the MPSP model exactly to obtain guaranteed optimal solutions is computationally expensive and not practical for real-time scheduling. A fast, high quality evolutionary heuristic, gMASH, is developed to quickly solve large problems. The MPSP model and the gMASH heuristic form a foundation for an intelligent medical procedures scheduling and optimization system.