The high-throughput measurement devices for DNA, RNA, and proteins produce large amount of information-rich data from biological dynamic systems. It is a need to reverse engineering these data to reveal parameters/structure and behavior relationships implicit in the data. Ultimately, complex interactions between its components that make up a system can be better understood. However, issues of reverse engineering in bioinformatics like algorithms use, the number of temporal sample, continuous or discrete type of input data, etc. are discussed but merely in the validity problem. We argue that, since the data available in reality are not so perfect, the result of reverse engineering is impacted by the un-perfect data. If this is true, to know how this impacts the results of the reverse engineering and to what extent is an important issue. We choose the parameter estimation as our task of reverse engineering and develop a novel method to investigate this validity problem. The data we used has a minor deviation from real data in each data point and then we compare the results of reverse engineering with its target parameters. It can be realized that the more error in data will introduce more serious validity problem in reverse engineering. Three artificial systems are used as test bed to demonstrate our approach. The results of the experiments show, a minor deviation in data may introduce large parameter deviation in the parameter solutions. We conclude that we should not ignore the data error in reverse engineering. To have more knowledge of this phenomenon, we further develop an analytical procedure to analyze the dynamic of the systems to see which characteristic will contribute to this impact. The sensitivity test, propagation analysis and impact factor analysis are applied to the systems. Some qualitative rules that describe the relationship between the results of reverse engineering and the dynamics of the system are summarized. All the finding of this exploration research needs more study to confirm its results. Along this line of research, the biological meaning and the possible relationship between robustness and the variation in parameters in reverse engineering is worth to study in the future. The better reverse algorithm to avoid this validity problem is another topic for future work.

In recent high-speed digital circuits, the simultaneous switching noise (SSN) or ground bounce noise (GBN) is induced due to the transient currents flowing between power and ground planes during the state transitions of the logic gates. In order to¡@analyze the effect of GBN on power delivery systems effectively and accurately, the impedance of power/ground is an important index to evaluate power delivery systems. In the operating frequency bandwidth, the power impedance must be less than the target impedance. The typical way to suppress the SSN is adding decoupling capacitors to create a low impedance path between power and ground planes. By using the admittance matrix method, we can evaluate the effect of decoupling capacitors mounted on PCB fast and accurately reducing the time needed from the empirical or try-and-error design cycle. In order to reduce the cost of decoupling capacitors, the genetic algorithm is employed to optimize the placement of decoupling capacitors to suppress the GBN. The decoupling capacitor are not effective in the GHz frequency range due to their inherent lead inductance. The electromagnetic bandgap(EBG) structure can produce a stopband to prevent the noise from disperseing at higher frequency. Combining decoupling capacitors with EBG structure to find the optimum placement for suppression of the SSN by using the genetic algorithm.

In this study, a design procedure incorporating a genetic algorithm (GA) is developed for optimization of structures. The objective function considered is the total weight of the structure. The objective function is minimized subjected to displacement and strength requirements. In order to evaluate the design constraints, finite element analysis are performed either by using conventional finite element solvers (i.e. MSC/NASTRAN&reg / ) or by using in-house codes. The application of the algorithm is shown by a number of design examples. Several strategies for reproduction, mutation and crossover are tested. Several conclusions drawn from the research results are presented.

In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems. In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA Penalty Scheme is used. The proposed method gives information about unfeasible individual fitness as near as possible to the feasible region in the evaluation function. The objective function in this study is the area of the structure. The area is minimized considering the Von-Misses stress criteria. In order to minimize the objective function, one-point crossover with roulette-wheel selection approach is used. Optimum dimensions of four problems available in the literature have been solved by the code developed . The algorithm is tested using several strategies such as / different initial population number, different probability of mutation and crossover. The results are compared with the ones in literature and conclusions are driven accordingly.

We use the elitist and extinction policies to improve the simple genetic algorithm in this study. We expect that the search technique can avoid falling into the local maximum due to the premature convergence, and the chance of finding the near-optimal parameter in the larger searching space could be obviously increased. The accelerometer is then taken as the sensor for output measurement, and the designed controller is implemented to actively suppress the vibration of the plain that is due to the excitation effect of the high-speed and precision positioning of the linear motor. From the computer simulations and the experimented results, it is obvious that the near-optimal controller designed by using genetic approach with elitist and extinction can improve the effect of vibration suppression; the settling time is also decrease. For the vibration suppressions of high-speed precision positioning problems, the results are satisfactory in the cases of short, middle and long distance.

ABSTRACT In this dissertation, several adaptive control design schemes for a class of nonlinear systems are proposed. The first topic of the research is concerned with self-tuning PID controller design. The main problem of designing PID controller is how to determine the values of three control gains, i.e., proportional gain , integral gain , and derivative gain . We attempt to use the technique of adaptive control based on the Lyapunov approach to design the PID controller for some class of partially known nonlinear systems. Three PID control gains are adjusted on-line such that better output performance can be achieved. The stability of the closed-loop PID control systems is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Second, two kinds of adaptive neural control systems including the direct and indirect neural controls are considered by using simple single auto-tuning neuron. We will first propose a novel neuron called auto-tuning neuron and use it to take place of the roles of the traditional neural networks used in the direct and indirect adaptive neural control systems. This can greatly reduce the computational time and network complexities due to the simple configuration of the auto-tuning neuron. It is also easy for hardware implementation. Third, based on the idea borrowed from natural evolution, genetic algorithm can search for optimal or near-optimal solutions for an optimization problem over the search domain. An optimization technique of real-coded genetic algorithm is used to design the PID controller by minimizing the performance index of integrated absolute error. The improvements of our results over that using other methods are also illustrated. In the last part of each section, some computer simulation results will also be provided to illustrate our proposed methods.

Synthetically generated terrain models are useful across a broad range of applications, including computer generated art & animation, virtual reality and gaming, and architecture. Existing algorithms for terrain generation suffer from a number of problems, especially that of being limited in the types of terrain that they can produce and of being difficult for the user to control. Typical applications of synthetic terrain have several factors in common: first, they require the generation of large regions of believable (though not necessarily physically correct) terrain features; and second, while real-time performance is often needed when visualizing the terrain, this is generally not the case when generating the terrain. In this thesis, I present a new, design-by-example method for synthesizing terrain height fields. In this approach, the user designs the layout of the terrain by sketching out simple regions using a CAD-style interface, and specifies the desired terrain characteristics of each region by providing example height fields displaying these characteristics (these height fields will typically come from real-world GIS data sources). A height field matching the user's design is generated at several levels of detail, using a genetic algorithm to blend together chunks of elevation data from the example height fields in a visually plausible manner. This method has the advantage of producing an unlimited diversity of reasonably realistic results, while requiring relatively little user effort and expertise. The guided randomization inherent in the genetic algorithm allows the algorithm to come up with novel arrangements of features, while still approximating user-specified constraints.

Because the government changed laws and opened the market progressively in recent years, the financial market in Taiwan becomes more and more liberal and international; every investor has to face a more complicated investitive environment. They can choice many investitive objects and tools, but how to choice the best one is a big problem for them and the risk in the financial market becomes much higher. Mutual fund is a popular investment tools in recent year. One of the mutual fund¡¦s benefits is the diversity of investment and effectively disperses risk.. In August 2006, the government in Taiwan opens up the market of mutual fund; the investors can buy offshore mutual funds in many channels, so they can choice many kinds of mutual funds, about 1,400 in April 2008. Also, every investor that can beat the level of risk is so different, it maybe make them confused and really want to know which one is much better and do asset allocation very well. Therefore, how to design a good portfolio for different perceived risk levels of investors is a worthful topic in the academia and the really world. This research uses the genetic algorithm to construct mutual fund portfolios based on perceived risk levels, use fund return, standard deviation, Alpha, Beta, Sharpe, IR and Sortino indicators to select funds of a portfolio and calculate portfolio return and standard deviation, then do asset allocation. This research change funds in every portfolio every month using Sliding Windows method from Jan 1, 2001 to Dec 12, 2007, totally 84 times. The result of this research is every portfolio average return wins benchmark index average return. The standard deviation of every portfolio also wins benchmark index standard deviation. It shows this research can beat benchmark index effectively and also can decrease the risk of portfolio return, then we can get a good fund portfolio for different perceived risk levels of investors

In this thesis we have proposed a variable-length multiobjective genetic algorithm to solve heterogeneous wireless transmitter placement with multiple constraints. Among many factors that may affect the result of placement, we focus on four major requirements, coverage, cost, data rate demand, and overlap. In the proposed algorithm we release the need for the upper bound number of transmitters that is a major constraint in the existing methods and achieve better wireless transmitter placement while considering the transmitter position and design requirement simultaneously. In experiments, we use the free space propagation model, the large scale propagation model which considers the shadowing effect, and the extended Hata-Okumura model to predict the path loss in a real two dimensional indoor environment, and an outdoor environment and even a real three dimensional outdoor environment. Experimental results show that the proposed algorithm can find many feasible solutions for all test cases under four objectives.

The manual trial and error has been widely used in the past, but such approach is inefficient. In recent years, many heuristic algorithms used in a wide range of applications have been developed. These algorithms have more efficiency than traditional ones, because they can locate the best solution. Every algorithm has its own niche application in different problems. In this study, the boundary parameters of the hydrodynamic-based tidal model are calibrated by using the Simulated Annealing algorithms (SA). The objective is to minimize the deviation between the estimated results acquired from the simulation model and the real tidal data along Taiwan coast. Based on the real physics distribution of the boundary parameters, we aimed to minimize the sum of each station¡¦s root mean square error (RMSE). Genetic Algorithms (GAs) and Simulated Annealing Algorithms on parameters calibration for tidal model are compared under the same condition. GAs is superior on solving the problems mentioned above while both algorithms showed improved results. By setting the initial solution derived from GAs, the solving efficiency of SA can be improved in this study.