Various approaches have been proposed for finding the longest common subsequence (LCS) of two sequences. The time complexities of these algorithms are usually $O(n^2)$ in the worst case, where $n$ is the length of input sequences. However, these algorithms would become infeasible when the input length, $n$, is very long. Recently, the $k$-LCS $(k ≥ 2)$ problem has become more attractive. Some algorithms have been proposed for solving the problem, but the execution time required for solving the $k$-LCS problem is still too long to be practical. In this thesis, we propose a genetic algorithm for solving the $k$-LCS problem with time complexity $O(Gpk(n + |P_j|))$, which $G$ is the number of generations, $p$ is the number of template patterns, $k$ is the number of input sequences, $n$ and $|P_j|$ are the length of input sequences and the length of template patterns, respectively. As our experimental results show, when $k$ is 20 and $n$ is 1000, the performance ratio ($|CS|/|LCS|$) of our algorithm is greater than 0.8, where $|CS|$ denotes the length of the solution we find, and $|LCS|$ represents the length of the real (optimal) LCS. Comparing the performance ratios with Expansion Algorithm and BNMAS Algorithm, our algorithm is much better than them when the number of input sequences varies from 2 to 20 and the length of the input sequences varies from 100 to 2000.

In this thesis, the Hybrid Genetic Algorithm-Ant Colony Optimization (GACO) approach is presented to solve the unit commitment problem (UC), and comparison with the results obtained using literature methods. Then this thesis applied the ability of the Genetic Algorithm (GA) operated after Ant Colony Optimization (ACO) can promote the ACO efficiency. The objective of GA is to improve the searching quality of ants by optimizing themselves to generate a better result, because the ants produced randomly by pheromone process are not necessary better. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. The other objective of this thesis is to investigate an influence of emission constraints on generation scheduling. The motivation for this objective comes from the efforts to reduce negative trends in a climate change. In this market structure, the independent power producers have to deal with several complex issues arising from uncertainties in spot market prices, and technical constraints which need to be considered while scheduling generation and trading for the next day. In addition to finding dispatch and unit commitment decisions while maximizing its profit, their scheduling models should include trading decisions like spot-market buy and sell. The model proposed in this thesis build on the combined carbon finance and spot market formulation, and help generators in deciding on when these commitments could be beneficial.

Wireless sensor network has applications in environmental surveillance, healthcare, and military operations. Because the energy of sensor nodes is limited and nodes are unable to supply energy in real time, the purpose of many researches is to prolong lifetime of sensor network. Lifetime is times that the sink can collect data from all sensor nodes. When a user proposes a query, then the sink gathers data from all sensor nodes. The problem defined in the previous research is given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then this problem is to find a directed tree that maximize minimum residual energy. In this thesis, we define a new problem that given a sensor network and residual energy of each node, and the energy consumption of transmitting a unit message between two nodes. Then our problem is to find a path of each node, which maximize minimum residual energy. We prove this problem is NP-complete. We propose a heuristic algorithm and a similar heuristic algorithm for this problem.

This thesis presents an imitation-based methodology, also a simple and easy way, for a service robot to learn the behaviors demonstrated by the user. With this proposed method, a robot can learn human behavior through observation. Inspired by the concept of biological learning, this learning model is initiated when facing a new learning event. A series of experiments are conducted to use a humanoid robot as a platform to implement the proposed algorithm. Discussions are made of how the robot generates a complete behavior sequences performed by its demonstrator. Because it is time consuming for a robot to go through the whole process of learning, we thus propose a decomposed learning method to enhance the learning performance, that is, based on the past learning information, the robot can skip learning again the behaviors already known. For simple robot behaviors, a hierarchical evolutionary mechanism is developed to evolve the complete behavior trajectories. For complex behaviors sequences, different ways are used to tackle the scalability problem, including decomposing the overall task into several sub-tasks, exploiting behavior information recorded previously, and constructing a new strategy to maintain population diversity. To verify our approach, a different series of experiments have been conducted. The results show that our imitation-based approach is a natural way to teach the robot new behaviors. This evolutionary mechanism successfully enables a humanoid robot to perform the behavior sequences it learns.

The most efficient algorithm for solving the elliptic curve discrete logarithm problem can only be done in exponential time. Hence, we can use it in many cryptographic applications. Weil pairing is a mapping which maps a pair of points on elliptic curves to a multiplicative group of a finite field with nondegeneracy and bilinearity. Pairing was found to reduce the elliptic curve discrete logarithm problem into the discrete logarithm problem of a finite field, and became an important issue since then. In 1986, Miller proposed an efficient algorithm for computing Weil pairings. Many researchers focus on the improvement of this algorithm. In 2006, Blake et al. proposed the reduction of total number of lines based on the conjugate of a line. Liu et al. expanded their concept and proposed two improved methods. In this paper, we use both NAF and segmentation algorithm to implement the Weil pairing and analyse its complexity.

The domain of computer music is an interesting area which combines computer science and music art. We propose a music accompaniment system using a variable-length genetic algorithm. Via the system one can make the music corresponded to his demands. In the style music accompaniment we analyze some important characteristic of pop music, and propose a new chromosome representation scheme to include the concept of rhyme, chord and melody. Chord progression is used as one of the evaluation criterions in this thesis. The system allows a user to input melody, to select emotion and rhyme, and the system will automatically generate the appropriate accompaniment based on the database compiled from some music theory relating to the chord progression. In addition, the system allows a user to select his favorite accompaniment that generated by the system. Based on the user selected accompaniment the system will generate similar accompaniments for the user.

This paper presents a search method of coupling between the laser and fiber , search method to make up through the fiber and the laser inter-position deviation caused optical transmission loss , this search method is applied in the array-alignment is different from the traditional single-alignment , the advantage for the method is simple, through the rough alignment then blind search and angle search , for fiber array find the coupling of the greatest optical power position. In the experiment there are four degrees of freedom to use precise stage to fine-tune the location of fiber array , to reduce losses due to tools to reach precise alignment of purpose ,so we prove our method can achieve the objective of the fiber-optic alignment in our experiments.

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<p>In this work, the graph database Neo4j developed by Neo Technology is presented together with some of it's functionality when it comes to accessing data as a graph. This type of data access brings the possibility to implement common graph algorithms on top of Neo4j. Examples of such algorithms are presented together with their theoretical backgrounds. These are mainly algorithms for finding shortest paths and algorithms for different graph measures such as centrality measures. The implementations that have been made are presented, as well as complexity analysis and the performance measures performed on them. The conclusions include that Neo4j is well suited for these types of implementations.</p>

De novo peptide sequencing from MS/MS spectra has become of primary importance in proteomics. It provides essential information for studies of protein structure and function. With the availability of various MS/MS spectra, a lot of computational methods have been developed to infer peptide sequences from them. However, current de novo peptide sequencing methods still have limitations. Some major ones include a lack of suitable models reflecting MS/MS spectra, limited information extracted from MS/MS spectra, and the inefficient use of multiple spectra. This thesis addresses some of the limitations with a series of novel computational methods designed for various MS/MS spectra and their combinations. The main content of the thesis starts with a comprehensive review of recent developments in de novo peptide sequencing methods, followed by two novel methods for single spectrum sequencing problems, and then presents two paired spectra sequencing methods. The first chapter introduces relevant background information, objectives of the study, and the structure of the thesis. After that, a comprehensive review of de novo peptide sequencing methods is given. It summarizes recent developments of computational methods for various experimental spectra, compares and analyzes their advantages and disadvantages, and points out some future research directions. Having these potential research directions, the thesis next presents two novel methods designed for higher-energy collisional dissociation (HCD) spectra and electron capture dissociation (ECD) (or electron transfer dissociation (ETD)) spectra, respectively. These methods apply new spectrum graph models with multiple types of edges, integrate amino acid combination (AAC) information and peptide tags, and consider spectrum-specific information to suit different spectra. After that, multiple spectra sequencing problem is studied. A framework for de novo peptide sequencing of multiple spectra is given with applications to two different spectra pairs. One pair is spectrally complementary to each other, and the other is similar spectra with property differences. These methods include effective spectra merging criteria and parent mass correction steps, and modify the previously proposed graph models to fit the merged spectra. Experiments on several experimental MS/MS spectra datasets and datasets pairs show the advantages of the proposed methods in terms of peptide sequencing accuracy. Finally, conclusions and future work directions are given at the end of the thesis. To summarize the work in the thesis, a series of novel computational methods for de novo peptide sequencing are proposed. These methods target different types of MS/MS spectra and their combinations. Experiential results show the proposed methods are either better than competing methods that already exist, or fill gaps in the suite of currently available methods.