Genetic algorithm is often used to find the global optimum in a multi-dimensional search problem. Inspired by the natural evolution process, this algorithm employs three reproduction strategies -- cloning, crossover and mutation -- combined with selection, to improve the population as the evolution progresses from generation to generation. Femtosecond laser pulse tailoring, with the use of a pulse shaper, has become an important technology which enables applications in femtochemistry, micromachining and surgery, nonlinear microscopy, and telecommunications. Since a particular pulse shape corresponds to a point in a highly-dimensional parameter space, genetic algorithm is a popular technique for optimal pulse shape control in femtosecond laser experiments. We use genetic algorithm to optimize third harmonic generation (THG), and investigate various pulse shaper options. We test our setup by running the experiment with varied initial conditions and study factors that affect convergence of the algorithm to the optimal pulse shape. Our next step is to use the same setup to control coherent anti-Stocks Raman scattering. The results show that the THG signal has been enhanced.

In power system reliability analysis, state space pruning has been investigated to improve the efficiency of the conventional Monte Carlo Simulation (MCS). New algorithms have been proposed to prune the state space so as to make the Monte Carlo Simulation sample a residual state space with a higher density of failure states. This thesis presents a modified Genetic Algorithm (GA) as the state space pruning tool, with higher efficiency and a controllable stopping criterion as well as better parameter selection. This method is tested using the IEEE Reliability Test System (RTS 79 and MRTS), and is compared with the original GA-MCS method. The modified GA shows better efficiency than the previous methods, and it is easier to have its parameters selected. This thesis also presents a Dynamic Programming (DP) algorithm as an alternative state space pruning tool. This method is also tested with the IEEE Reliability Test System and it shows much better efficiency than using Monte Carlo Simulation alone.

Hydrocarbon use has been increasing and will continue to increase for the foreseeable future in even the most pessimistic energy scenarios. Over the past few decades, natural gas has become the major player and revenue source for many countries and multinationals. Its presence and power share will continue to grow in the world energy mix. Much of the current gas reserves are found in gas condensate reservoirs. When these reservoirs are allowed to deplete, the pressure drops below the dew point pressure and a liquid condensate will begin to form in the wellbore or near wellbore formation, possibly affecting production. A field optimization includes determining the number of wells, type (vertical, horizontal, multilateral, etc.), trajectory and location of wells. Optimum well placement has been studied extensively for oil reservoirs. However, well placement in gas condensate reservoirs has received little attention when compared to oil. In most cases involving a homogeneous gas reservoir, the optimum well location could be determined as the center of the reservoir, but when considering the complexity of a heterogeneous reservoir with initial compositional variation, the well placement dilemma does not produce such a simple result. In this research, a horizontal well placement problem is optimized by using a modified Genetic Algorithm. The algorithm presented has been modified specifically for gas condensate reservoirs. Unlike oil reservoirs, the cumulative production in gas reservoirs does not vary significantly (although the variation is not economically negligible) and there are possibly more local optimums. Therefore the possibility of finding better production scenarios in subsequent optimization steps is not much higher than the worse case scenarios, which delays finding the best production plan. The second modification is developed in order to find optimum well location in a reservoir with geological uncertainties. In this modification, for the first time, the probability of success of optimum production is defined by the user. These modifications magnify the small variations and produce a faster convergence while also giving the user the option to input the probability of success when compared to a Standard Genetic Algorithm.

It is well known that the biological function of a protein depends on its 3D structure. Therefore, solving the problem of protein structures is one of the most important works for studying proteins. However, protein structure prediction is a very challenging task because there is still no clear feature about how a protein folds to its 3D structure yet. In this thesis, we propose a genetic algorithm (GA) based on the lattice model to predict the 3D structure of an unknown protein, target protein, whose primary sequence and secondary structure elements (SSEs) are assumed known. Hydrophobic-hydrophilic model (HP model) is one of the most simplified and popular protein folding models. These models consider the hydrophobic-hydrophobic interactions of protein structures, but the results of prediction are still not encouraged enough. Therefore, we suggest that some other features should be considered, such as SSEs, charges, and disulfide bonds. That is, the fitness function of GA in our method considers not only how many hydrophobic-hydrophobic pairs there are, but also what kind of SSEs these amino acids belong to. The lattice model is in fact used to help us get a rough folding of the target protein, since we have no idea how they fold at the very beginning. We show that these additional features do improve the prediction accuracy by comparing our prediction results with their real structures with RMSD.

This thesis is based on the DSP to develop a facial expression recognition system. Most facial expression recognition systems suppose that human faces have been found, or the background colors are simple, or the facial feature points are extracted manually. Only few recognition systems are automatic and complete. This thesis is a complete facial expression system. Images are captured by CCD camera. DSP locates the human face, extracts the facial feature points and recognizes the facial expression automatically. The recognition system is divided into four sub-system: Image capture system, Genetic Algorithm human face location system, Facial feature points extraction system, Fuzzy logic facial expression recognition system. Image capture system is using CCD camera to capture the facial expression image which will be recognized in any background, and transmitting the image data to SRAM on DSP through the PPI interface on DSP. Human face location system is using genetic algorithm to find the human face¡¦s position in image by facial skin color and ellipse information, no matter what the size of the human face or the background is simple. Feature points extraction system is finding 16 facial feature points in located human face by many image process skills. Facial expression recognition system is analyzing facial action units by 16 feature points and making them fuzzily. Judging the four facial expression: happiness, anger, surprise and neutral, by fuzzy rule bases.. According to the results of the experiment. The facial expression system has nice performance on recognition rate and recognition speed.

DNA microarrays are widely used techniques in molecular biology and DNA computing area. Before performing the microarray experiment, a set of subsequences of DNA called probes which are complementary to the target genes of interest must be found. And its reliability seriously depends on the quality of the probe sequences. Therefore, one must carefully choose the probe set in target sequences. A new method for probe design strategy using multi-objective genetic algorithm is proposed. The proposed algorithm is able to find a set of suitable probes more efficient and uses a model based on suffix tree to speed up the specificity constraint checking. The dry dock experimental results show that the proposed algorithm finds several probes for DNA microarray that not only obey the design properties, but also have specificity.

The implicit function model is nowadays generally applied to a lot of fields that need 3D, such as computer game, cartoon or for specially effect film. So far, most hardware are still to support the polygon-mesh model but not implicit function model, so polygon-mesh model is still the mainstream of computer graphics. However, translation between the two representation models becomes a new research topic. This paper presents a new method to translate the triangles mesh model into the implicit functions model. The main concept is to use the binary space-partitioning tree to divide the points and patches in the triangle mesh model to create a hierarchical structure. For each leaf node in this hierarchical structure, we would generate a corresponding implicit function. These implicit functions are generated by the genetic algorithm. And the internal nodes in this hierarchical structure are blended by the blending operators. The blending operators make the surface become smooth and continual. The method we proposed reduces the data in a large amount because we only save the coefficients of the implicit surface. And the genetic algorithm can avoid the high computing complexity.

Taguchi methods is also called quality engineering. It is a systematic methodology for product design(modify) and process design(improvement) with the most of saving cost and time, in order to satisfy customer requirement. Taguchi¡¦s parameter design is also known as robust design, which has the merits of low cost and high efficiency, and can achieve the activities of product quality design, management and improvement, consequently to reinforce the competitive ability of business. It is a worthy research course to study how to effectively apply parameter design, to shorten time spending on research, early to promote product having low cost and high quality on sale and to reinforce competitive advantage. However, the parameter design optimization problems are difficult in practical application owing to (1)complex and nonlinear relationships exist among the system¡¦s inputs, outputs and parameters and (2)interactions may occur among parameters. (3)In Taguchi¡¦s two-phase optimization procedure, the adjustment factor cannot be guaranteed to exist in practice. (4)For some reasons, the data may become lost or were never available. For these incomplete data, the Taguchi¡¦s method cannot treat them well. Neural networks have learning capacity fault tolerance and model-free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful field including diagnostics, robotics, scheduling, decision-marking, predicition, etc. In the process of searching optimization, genetic algorithm can avoid local optimization. So that it may enhance the possibility of global optimization. This study had drawn out the key parameters from the spheroidizing theory, and L18, L9 orthogonal experimental array were applied to determine the optimal operation parameters by Signal/Noise analysis. The conclusions are summarized as follows: 1. The spheroidizing of AISI 3130 used to be the highest unqualified product, and required for the second annealing treatment. The operational record before improvement showed 83 tons of the 3130 steel were required for the second treatment. The optimal operation parameters had been defined by L18(61¡Ñ35) orthogonal experimental array. The control parameters of the annealing temperature was at B2

In this thesis, we propose an improved fuzzy GBML¡]genetic-based machine learning¡^algorithm to construct a FRBCS¡]fuzzy rule-based classification system¡^for pattern classification problem. Existing hybrid fuzzy GBML algorithm is consuming more computational time since it used the SS fuzzy model and combined with the Michigan-style algorithm for increasing the convergent rate of the Pittsburgh-style algorithm. By contrast, our improved fuzzy GBML algorithm is consuming less computational time since it used the MW fuzzy model and instead of the role of the Michigan-style algorithm by a heuristic procedure. Experimental results show that improved fuzzy GBML algorithm possesses the shorter computational time, the faster convergent rate, and the slightly better classification rate.

Automatic test data generation is a hot topic in recent software testing research. Various techniques have been proposed with different emphases. Among them, most of the methods are based on Genetic Algorithms (GA). However, whether it is the best Metaheuristic method for such a problem remains unclear. In this paper, we choose to use another approach which arms a GA with an intensive local searcher (the so-called Memetic Algorithm (MA) according to the recent terminology). The idea of incorporating local searcher is based on the observations from many real-world programs. It turns out the results outperform many other known Metaheuristic methods so far. We argue the needs of local search for software testing in the discussion of the paper.