This article investigates the detection and identification problems for changing of regimes about non-linear time series process. We apply the concept of genetic algorithm and AIC criterion to test the changing of regimes. This way is different from traditional detection methods According to our statistical decision procedure, the mean of moving average and the genetic detection for the underlying time series will be considered to decide change points. Finally, an empirical application about the detection and identification of change points for the Taiwan Business Cycle is illustrated.

This project presents the development of a collision avoidance active system for cars.There is a large interest in developing avoidance system in the automotive industry since the accidents are of such nature that can be avoided if the system works as desirable e.g., in animal crossing or having the car in front stopping without the driver noticing. A control system is designed to avoid collisions by acting on the steer and brakes of a car. An algorithm is developed to optimize a fuzzy logic controller which actuates on the steer and brakes of the car. The algorithm optimizes the inputs of the car, i.e. steer and brake, to avoid the collision with the object. The optimization of the trajectory implies that the car returns to the original lane and it is the minimum time possible inthe other lane. The object is situated at different distances and the initial speed of the car also varies depending on the situations. The results are obtained by using a car model that is developed in this project in conjunction with the tyre model, [1]. Simulations show that it performs collision avoidance manoeuvres in different conditions. Furthermore, improvements of the present work are suggested that are believed to further enhance the presented algorithm.

For a commercial port, to efficiently schedule the public berths is an important issue. Since a berth schedule would affect the usage of the commercial port, in this thesis, we apply a genetic algorithm to schedule the public berths in order to minimize the total waiting time of vessels. When in the initialization process, we encode the chromosome based on wharf characteristics in order to avoid assigning vessels to inappropriate wharves. After mutation process, we also adjust the usage of wharves to improve the speed of convergence speed. Simulation results show that the proposed algorithm can assign vessels to proper berths as soon as vessels arrive. Compared to the other genetic algorithms, the proposed algorithm obtains better performance in convergence speed and the quality of the solutions.

Water quality models are applied to assess the various processes affecting the concentrations of contaminants in a watershed. SPAtially Referenced Regression On Watershed attributes (SPARROW) is a nonlinear regression based approach to predict the fate and transport of contaminants in river basins. In this research SPARROW was applied to the Guadalupe and San Antonio River Basins of Texas to assess E. coli contamination. Since SPARROW relies on the measured records of concentrations of contaminants collected at monitoring stations for the prediction, the effect of the locations and selections of the monitoring stations was analyzed. The results of SPARROW application were studied in detail to evaluate the contribution from the statistically significant sources. For verification of SPARROW application, results were compared to 303 (d) list of Clean Water Act, 2000. Further, a methodology to maintain the monitoring records of the highly contaminated areas in the watersheds was explored with the application of the genetic algorithm. In this study, the importance of the available scale and details of explanatory variables (sources, land-water delivery and reservoir/ stream attenuation factors) in predicting the water quality processes were also analyzed. The effect of uncertainty in the monitored records on SPARROW application was discussed. The application of SPARROW and genetic algorithm were explored to design a monitoring network for the study area. The results of this study show that SPARROW model can be used successfully to predict the pathogen contamination of rivers. Also, SPARROW can be applied to design the monitoring network for the basins.

Oligo array is a high throughput technology and is widely used in many scopes of biology and medical researches for quantitative and highly parallel measurements of gene expression. When one faulty step occurs during the synthesis process, it affects all probes using the faulty step. In this thesis, a two-phase genetic algorithm (GA) is proposed to design optimal quality control of oligo array for detecting any single faulty step. The first phase performs the wide search to obtain the approximate solutions and the second phase performs the local search on the approximate solutions to achieve the optimal solution. Besides, the proposed algorithm could hold many non-duplicate individuals and parallelly search multiple regions simultaneously. The superior searching capability of the two-phase GA helps us to find out the 275 nonequireplicate cases that settled by the hill-climbing algorithm. Furthermore, the proposed algorithm also discovers five more open issues.

The multiplex PCR experiment is to amplify multiple regions of a DNA sequence at the same time by using different primer pairs. Although, in recent years, there are lots of methods for PCR primer design, only a few of them focus on the multiplex PCR primer design. The multiplex PCR primer design is a tedious task since there are too many constraints to be satisfied. A new method for multiplex PCR primer design strategy using genetic algorithm is proposed. The proposed algorithm is able to find a set of suitable primer pairs more efficient and uses a MAP model to speed up the examination of the specificity constraint. The dry-dock experiment shows that the proposed algorithm finds several sets of primer pairs for multiplex PCR that not only obey the design properties, but also have specificity.

In this thesis, we propose and evaluate algorithms for solving the worst-case-coverage deployment problem in ad-hoc wireless sensor networks. The worst-case-coverage deployment problem is to deploy additional sensors in the wireless sensor field to optimize the worst-case coverage. We derive a duality theorem that reveals the close relation between the maximum breach path and the minimum Delaunay cut. The duality theorem is similar to the well-known max-flow-min-cut theorem in the field of network optimization. The major difference lies in the fact that the object function we study in this paper is nonlinear rather than linear. Based on the duality theorem, we propose an efficient dual algorithm to solve the worst-case-coverage deployment problem. In addition, we propose a genetic algorithm for deploying a number of additional sensors simultaneously. We use analytical proofs and simulation results to justify the usage of the proposed approaches.

The thesis illustrates the development of DSP-based ¡§Relativity Gene Algorithm For Multiple Faces Recognition System＂. The recognition system is divided into three systems: Ellipsoid location system of multiple human faces, Feature points and feature vectors extraction system, Recognition system algorithm of multiple human faces. Ellipsoid location system of multiple human faces is using CCD camera or digital camera to capture image data which will be recognized in any background, and transmitting the image data to SRAM on DSP through the PPI interface on DSP. Then, using relatively genetic algorithm with the face color of skin and ellipsoid information locate face ellipses which are any location and size in complex background. Feature points and feature vectors extraction system finds facial feature points in located human face by many image process skills. Recognition system algorithm of multiple human faces is using decision by majority. Using characteristic vectors compares every vector in the database. Then, we draw out the highest ID. The recognizable result is over. The experimental result of the developed recognition system demonstrates satisfied and efficiency.

Since the cost of software testing has continuously accounted for large proportion of the software development total cost, automatic test data generation becomes a hot topic in recent software testing research. These researches attempt to reduce the cost of software testing by generating test data automatically, but they are discussed only for the single version programs not for the programs which are needed re-testing after changing. On the other hand, the regression testing researches discuss about how to re-test programs after changing, but they don¡¦t talk about how to generate test data automatically. Therefore, we propose an automatic test data evolution strategy in this paper. We use the method of regression testing to find out the part of programs which need re-testing, then automatic evolutes the test data by hybrid genetic algorithm. According to the experiment result, our strategy has the same or better testing ability but needs less cost than the other strategies.

Unit commitment is one of the most important subjects with respect to the economical operation of power systems, which attempts to minimize the total thermal generating cost while satisfying all the necessary restrictive conditions. ¡@¡@This thesis proposes a short-term thermal generating unit commitment by genetic algorithm and back propagation network. Genetic algorithm is based on the optimization theory developed from natural evolution principles, and in the optimization process, seeks a set of solutions simultaneously rather than any single one by adopting stochastic movement rule from one solution to another, which prevents restriction to fractional minimal values. Neural networks method outperforms in speed and stability. This thesis uses back propagation network method to complete neural networks and sets the optimal unit combination derived from genetic algorithm as the target output. ¡@¡@Under fixed electrical systems, instant responsiveness can be calculated by neural networks. When the systematical architecture changes, genetic algorithm can be applied to re-evaluation of the optimal unit commitment, hoping to improve the pitfalls of traditional methods. ¡@¡@This thesis takes the power system of six units for example to conduct performance assessment. The results show that genetic algorithm provides solutions closer to the overall optimal solution than traditional methods in optimizing unit commitment. On the other hand, neural networks method can not only approximate the solution obtained by genetic algorithm but also process faster than any other methods.