The basic idea of feature selection, as the name suggests, is to select an appropriate subset of features of a problem in question so as to reduce the data complexity, thus accelerating the analysis process. Being an NP-hard problem, it has become a critical research topic in data mining for years. In this study, we propose a novel, effective metaheuristic algorithm, called search economics for feature selection, for solving this problem. The proposed algorithm is built on the idea of investing in different sectors of the market based on the potential of each sector; in other words, it is built on the idea of searching different regions in the solution space based on the so-called potential of each region. By using such a search strategy, the proposed algorithm is less likely to fall into local optimum at early iterations while at the same time retaining the search diversity during the convergence process. The simulation results show that the proposed algorithm provides a better result than all the other state-of-the-art feature selection algorithms compared in this study in solving the eighteen well-known UCI datasets.

The vehicle routing problem is a critical research issue in logistics because the strategies used may strongly impact its performance. A good strategy would allow the management to make a good decision to enhance the performance of logistics. In practice, we have to consider two or more factors in solving the vehicle routing problem at the same time. An effective hybrid search algorithm for the vehicle routing problem with time windows is presented in this study, which takes into account at the same time the number of vehicles and the total distance for vehicles based on a novel metaheuristic algorithm named coral reefs optimization algorithm with substrate layers (CRO-SL). The experimental results show that the proposed algorithm outperforms all the other state-of-the-art algorithms compared in this paper.

Since the number of vehicles on the road has been dramatically increased, how to find out a “good solution” to improve the traffic situation is a difficult optimization problem even if there is still plenty of room for improvement. The main focus of this study is on improving the quality of solutions for the traffic light optimization problem to minimize the waiting time of all the vehicles and maximize the number of vehicles arriving at the destination within a certain time period. An effective hybrid metaheuristic algorithm, called grey wolf grasshopper hybrid algorithm (GWGHA), that leverages the strength of grey wolf optimizer and grasshopper optimization algorithm will be presented in this paper to find a better result in solving the cycle traffic light problem (CTLP). The solutions obtained are simulated with a well-known microscopic traffic simulator named Simulation of Urban MObility (SUMO). Experiments were carried out based on data from Kaohsiung, Taiwan, Bahıa Blanca, Argentina, and Ma laga, Spain that represent different experimental cases to evaluate the performance of GWGHA. The simulation results show that the proposed algorithm is superior to the state-of-the-art search algorithms compared in this paper for solving the CTLP.

An improved quantum-inspired evolutionary algorithm (iQEA) is presented in this paper to improve the clustering result of a data clustering problem. Like the other QEA-based algorithms, the iQEA uses Q-bits to denote the state of a quantum particle and Q-gate as an evolutionary operator to guide the search directions. Unlike the fixed rotation degree of QEAs, the rotation degree of iQEA will be changed at different iterations. Experimental results show that the iQEA is able to find a better result than all the other metaheuristic algorithms compared in this paper in terms of quality.

There is no doubt that cloud computing provides a simple but efficient way to share computational resources we have for an information system. Developing a highly effective method for solving the workflow scheduling problem (WSP) is one critical issue for enhancing the performance of such a system that requires to minimize the execution time, rent costs of resources, and transmission costs of data in a cloud workflow management system. This paper presents an efficient search algorithm, which is an extended version of search economics (SE), to solve WSP in a cloud computing environment, called search economics for workflow scheduling algorithm (SEWSA). The proposed algorithm inherits the distinguishing features of SE that uses not only the objective value to make a decision for the later search directions but also the potential of the subsolution space. The experimental results show that the proposed algorithm can find a better scheduling strategy to optimize the makespan time and to reduce the total cost than other metaheuristic algorithms.

How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs the k-means as the local search operator to fine-tune the end result, is presented in this paper. Simulation results show that the proposed algorithm is less sensitive to the initial random solution; thus, it is capable of providing a better result than the other clustering algorithms compared in this paper in terms of the quality of the clustering result.

One of the most well-known clustering methods for wireless sensor network is, no doubt, the so-called low energy adaptive clustering hierarchy (LEACH) because it is simple and easy to implement. Although LEACH tries to provide a fair selection mechanism by randomly selecting a number of sensors as the cluster-heads, it does not take into account the distribution of sensors, the main reason that LEACH is not able to allot transmissions to sensors efficiently, so it will make sensors far from the base station consume more energy in some cases. An efficient clustering algorithm to overcome this problem is presented in this paper. The proposed algorithm leverages the strength of a novel metaheuristics, search economics, and LEACH-centralized (LEACH-C) for wireless sensor network (WSN). Simulation results show that the search economics based clustering algorithm is capable of not only prolonging the lifetime of a WSN but also providing a balance strategy for the energy consumption of sensors in a homogeneous WSN.

In data mining, finding frequent itemsets is a critical step to discovering association rules. The number of frequent itemsets may, however, be huge if the threshold of minimum support is set at a low value or the number of items in the transaction database to be mined is large. In the past, some a pproaches were thus proposed to keep frequent itemsets with compact representation. For example, the approach of maximal itemsets keeps a borderline composed of the maximal itemsets, which separate frequent itemsets from non-frequent ones. It can recover all the frequent itemsets, but cannot get their actual frequencies back. On the contrary, the approach of closed itemsets can correctly recover each frequent itemset and its frequency. Besides, another approach called reference itemsets can recover each frequent itemset and approximately estimate its frequency. In this paper, we propose an efficient algorithm to recover each frequent itemset and its approximate frequency based on the kept maximal itemsets, frequent 1-itemsets, their supports, and some key information. The maximal frequent itemsets are used to recover all frequent itemsets, which are then organized into a simple flow network with levels. Next, the kept key information is used to derive approximate supports of the frequent itemsets in the flow network through the flow process. Finally, a series of experiments are conducted to show the compression effects of the proposed algorithm.

A high-performance scheduling algorithm, called MapReduce coral reef (MRCR), is presented in this paper. The basic idea of the proposed algorithm is to apply the MapReduce programming model of Spark to the coral reef optimization algorithm to speed up its response time. This means that MRCR will distribute its solutions to as many cluster nodes as it needs for calculating the objective value of solutions using the MapReduce technology. To evaluate the performance of the proposed algorithm, several benchmarks are used with different algorithms. Our experimental results show that the proposed algorithm is able to speed up the response time of the coral reef optimization algorithm in solving complex optimization problems, especially when the size of benchmarks is large.

One of the key reasons that traditional clustering methods are inefficient for analyzing large-scale datasets is because most of them are designed for a centralized system. This means that if the size of input data exceeds the size of storage or memory of such a system, it would make the task of clustering much more difficult. To mitigate this problem, an efficient clustering algorithm, called MapReduce Black Hole (MRBH), is presented in this paper to leverages the strength of the black hole algorithm and the MapReduce programming model of Hadoop to accelerate the clustering speed by both software and hardware. By using MapReduce, MRBH will then divide a large dataset into a number of small data sets and cluster these smaller data sets in parallel. Moreover, MRBH inherits the characteristics of the black hole algorithm, meaning that no parameters are to be set manually, thus, the implementation is easy. To evaluate the performance of the proposed algorithm, several datasets are used with different numbers of nodes. Experimental results show that the proposed algorithm can provide a significant speedup as the number of nodes increases.

A user may be influenced by the other users of a social network by sharing information. Influence maximization is one of the critical research topics aimed at knowing the current circumstances of a social network, such as the general mood of the society. The goal of this problem is to find a seed set which has a maximum influence with respect to a propagation model. For the influence maximization problem is NP-Hard, it is obvious that an exhausted search algorithm is not able to find the solution in a reasonable time. It is also obvious that a greedy algorithm may not find a solution that satisfies all the requirements. Hence, a high-performance algorithm for solving the influence maximization problem, which leverages the strength of the greedy method and the genetic algorithm (GA) is presented in this paper. Experimental results show that the proposed algorithm can provide a better result than simple GA by about 10% in terms of the quality.

The so-called multi-objective optimization problem (MOP) has become a critical research area because many MOPs exist in our daily life and solutions to these problems may strongly impact the performance of systems we use. Unlike solving a single-objective problem, solving a MOP requires that many conflicting objectives be optimized altogether at the same time. Since most MOPs are NP-hard, how to find an approximate solution using a limited computation resource has become an active research topic in recent years. In this paper, we present a high-performance algorithm for solving the MOP that leverages the strengths of firefly algorithm and non-dominated sorting genetic algorithm II (NSGA-II). To evaluate the performance of the proposed algorithm, we apply it to several MOPs. Simulation results show that the proposed algorithm can essentially provide a better result than all the state-of-the-art multi-objective optimization algorithms compared in this paper in most cases.

Until now, most, if not all, of the metaheuristic algorithms have been extremely sensitive to the initial solutions and may even converge to a local optimum at early iterations for most optimization problems. This paper introduces an effective and efficient framework, called multiple-search multi-start (MSMS), to mitigate the impact of these problems. To evaluate the performance of the proposed framework, we apply it to k-means and particle swarm optimization for the clustering problem and compare the results with those of several well-known clustering algorithms. The experimental results show that the proposed framework can significantly enhance the performance of not only single-solution-based but also population-based metaheuristic algorithms in terms of both the quality and the computation time.

Clustering has always been playing a vital role in many different disciplines because it is an important tool for analyzing a set of unknown input patterns. However, some important issues related to clustering, such as automatically determining the number of clusters and partitioning non-linearly separable data, are never fully solved even though many researchers work on this subject for a long time. As such, a novel method based on the so called elastic net clustering algorithm is presented in this paper to deal with exactly the two issues: partitioning non-linearly separable data and automatically determining the number of clusters. To evaluate the performance of the proposed algorithm, several well-known datasets are used. The experimental results show that not only can the proposed algorithm find the appropriate number of clusters, but it can also provide a higher accuracy rate than all the other methods compared in this study for most datasets.

Rule-based scheduling algorithms have been widely used on many cloud computing systems because they are simple and easy to implement. However, there is plenty of room to improve the performance of these algorithms, especially by using heuristic scheduling. As such, this paper presents a novel heuristic scheduling algorithm, called hyper-heuristic scheduling algorithm (HHSA), to find better scheduling solutions for cloud computing systems. The diversity detection and improvement detection operators are employed by the proposed algorithm to dynamically determine which low-level heuristic is to be used in finding better candidate solutions. To evaluate the performance of the proposed method, this study compares the proposed method with several state-of-the-art scheduling algorithms, by having all of them implemented on CloudSim (a simulator) and Hadoop (a real system). The results show that HHSA can significantly reduce the makespan of task scheduling compared with the other scheduling algorithms evaluated in this paper, on both CloudSim and Hadoop.

There is no doubt about the potentials of smart grid, for the traditional power grid has been kind of out of date, in terms of not only its infrastructure but also its restrictions on the way information is communicated. To provide better and smarter services via smart grid, the first thing we have to do is to make it more intelligent. Among the technologies that can be applied to smart grid to make it more intelligent, data mining will certainly play a vital role. This paper begins with a brief introduction to smart grid, followed by a discussion on the supervised learning (classification) and the unsupervised learning (clustering). Several open and possible research issues are then given to depict the future trends of smart grid.

As one of the traditional optimization problems, clustering still plays a vital role for the researches both theoretically and practically nowadays. Although many successful clustering algorithms have been presented, most (if not all) need to be given the number of clusters before the clustering procedure is invoked. A novel differential evolution based clustering algorithm is presented in this paper to solve the problem of determining the number of clusters automatically. The proposed algorithm leverages the strengths of two technologies: one is a novel algorithm for finding the approximate number of clusters while the other is a heuristic search algorithm for automatic clustering. The experimental results show that the proposed algorithm can not only determine the approximate number of clusters automatically, but it can also provide more accurate results.

Automatically determining the number of clusters without a priori knowledge is a difficult research issue for data clustering problem. An effective multiobjective evolutionary algorithm based clustering algorithm is proposed to not only overcome this problem but also provide a better clustering result in this study. The proposed algorithm differs from the traditional evolutionary algorithm in the sense that instead of a single crossover operator and a single mutation operator, the proposed algorithm uses a pool of crossover operators and a pool of mutation operators that are selected at random to increase the search diversity. To evaluate the performance of the proposed algorithm, several well-known datasets are used. The simulation results show that not only can the proposed algorithm automatically determine the number of clusters, but it can also provide a better clustering result.

The so-called heuristics have been widely used in solving combinatorial optimization problems because they provide a simple but effective way to find an approximate solution. These technologies are very useful for users who do not need the exact solution but who care very much about the response time. For every existing heuristic algorithm has its pros and cons, a hyper-heuristic clustering algorithm based on the diversity detection and improvement detection operators to determine when to switch from one heuristic algorithm to another is presented to improve the clustering result in this paper. Several well-known datasets are employed to evaluate the performance of the proposed algorithm. Simulation results show that the proposed algorithm can provide a better clustering result than the state-of-the-art heuristic algorithms compared in this paper, namely, k-means, simulated annealing, tabu search, and genetic k-means algorithm.

This paper presents a novel pheromone update strategy for improving the clustering results of ant colony optimization (ACO). The proposed algorithm is motivated by the observation that most of the ACOs only keep track of the promising foraging information, which has the potential to lead to better solutions than all the other search directions in the pheromone table. This eventually makes the search converge to particular search directions in later iterations because the pheromone values on good routing paths will be reinforced. As such, the breadth of search (diversity) will be reduced, thus limiting the clustering results of ACO. The proposed algorithm adds a second pheromone table to ACO for recording the unpromising foraging information that is worse than all the other search directions and using a novel construction method to explore the new search directions. In other words, by leveraging the strengths of diversification and intensification, the proposed algorithm can find better solutions than traditional ACO. To evaluate the performance of the proposed algorithm, we use it to solve the data clustering problem. Our experimental results indicate that the proposed algorithm can significantly improve the quality of ant colony optimization.

This paper presents a novel ant colony optimization (ACO) based on an efficient solution construction strategy (transition operator) for improving the quality of the end results of job shop scheduling problem (JSSP). Inspired by the observation that the quality of the end results of ACO is largely affected by their operators-especially the transition operator, a novel solution construction strategy is presented in this paper. The proposed algorithm uses two different strategies to compute the probability of solution construction to improve the end results. Our experimental results show that the proposed algorithm outperforms all state-of-the-art job shop scheduling algorithms evaluated in this paper and can significantly improve the quality of ant colony optimization for JSSP.

This paper presents a novel algorithm for tagging the relationship of test on the learning content called Binary Union-plus Tag (BUT). The proposed algorithm is motivated by the desire of students to re-study unacquainted content after a test. By binary coding items of content, BUT provides the users of an e-Learning system a certain range of content which lacks learning to connect to tests and content. It starts off with one of the content structures as a reference structure. Then, all the mistakes made on the test are mapped to the reference structure to highlight the re-studying area in the learning materials. To evaluate the performance of the proposed method, it has been used on the learning process of two hundred grade students. Moreover, the experimental results show that the proposed algorithm can considerably improve the achievement of students in terms of both locating the essential range of the content and providing the necessary matters.

When a mining procedure is directly executed on very large databases, the computer memory may not allow the processing in memory. In the past, we adopted a branch-and-bound search strategy to divide the domain items as a set of groups. Although it works well in partitions the items, the time is quite time consuming. In this paper, we thus propose a GA-based approach to speed up the partition process. A new encoding representation and a transformation scheme are designed to help the search process. Experimental results also show that the algorithm can get a proper partition with good efficiency.

In this paper, we present a fast cycle-accurate instruction set simulator (CA-ISS) for system-on-chip development based on QEMU and SystemC. Even though most state-of-the-art commercial tools have tried very hard to provide all the levels of details to satisfy the different requirements of the software designer, the hardware designer, and even the system architect, the hardware/software co-simulation speed is dramatically slow when co-simulating the hardware models at the register-transfer level (RTL) with a full-fledged operating system (OS). Our experimental results show that the combination of QEMU and SystemC can make the co-simulation at the CA level much faster than the conventional RTL simulation, even with a full-fledged operating system up and running. Furthermore, the statistics indicate that with every instruction executed and every memory accessed since power-on traced at the CA level, it takes 28m15.804s on average to boot up a full-fledged Linux kernel, even on a personal computer. Compared to the kernel boot time reported by Xilinx and SiCortex, the proposed CA-ISS is about 6.09 times faster compared to “SystemC without trace” of Xilinx and about 30.32 times faster compared to “SystemC models converted from RTL” of SiCortex. The main contributions of this paper are threefold: 1) a hardware/software co-simulation environment capable of running a full-fledged OS at the early stage of the electronic system level design flow at an acceptable simulation speed is proposed; 2) a virtual platform constructed using the proposed CA-ISS as the processor model can be used to estimate the performance of a target system from system perspective, which all the previous works, such as QEMU-SystemC, do not provide; and 3) such a virtual platform also provides the modeling capability from the transaction level down to the CA level or the other way around.

By and large, population-based metaheuristics provide a better solution to the codebook generation problem of vector quantization (VQ) than Generalized Lloyd Algorithm (GLA) and single-solution-based metaheuristics. They are, however, all much slower. In this paper, we present an efficient method to speed up the performance of particle swarm optimization (PSO), called Fuzzy Pattern Reduction Enhanced Particle Swarm Optimization (FPREPSO). The proposed method first uses PSO to search for the global solutions. Then, it relies on pattern reduction to eliminate computations that are essentially redundant in the convergence process of PSO. And finally, it employs a set of fuzzy inference rules to decrease the chance of eliminating patterns that should not be eliminated. To evaluate the performance of the proposed algorithm, we compare it with GLA and GLA-based algorithms such as standard GLA, pattern reduction enhanced GLA, genetic k-means algorithm (GKA), and PSO. Our simulation results show that the proposed algorithm can cut the computation time down by 51.07% up to 81.77% compared to PSO and GKA.

In this paper, we present an efficient method for speeding up Ant Colony Optimization (ACO), called Pattern Reduction Enhanced Ant Colony Optimization (PREACO). The proposed algorithm is motivated by the observation that many of the computations of ACO on its convergence process are essentially redundant and thus can be eliminated to reduce its computation time. To evaluate the performance of the proposed algorithm, we use it to solve the the traveling salesman problem (TSP). Moreover, we compare the proposed algorithm with several state-of-the-art ACO-based algorithms. Our simulation results indicate that the proposed algorithm can reduce the computation time of ACO algorithms we evaluated up to 99.21% or by a factor of 126.58 while limiting the degradation of the quality of the solution to a very small percentage compared to ACO algorithms themselves.

This paper presents a novel framework based on the notion of pattern reduction, called Framework for Accelerating Metaheuristics via Pattern Reduction (FAMPR), to solve an intrinsic problem of metaheuristics. That is, many computations of metaheuristics during the convergence process are essentially redundant. As such, if they can be eliminated, the computation time of metaheuristics can be significantly reduced. Our experimental result shows that the proposed framework can significantly reduce the computation time of metaheuristics while limiting the loss of quality to a very small percentage.

In this paper, we present a simple but efficient algorithm for integrating a collection of heterogeneous itembanks, called heterogeneous itembanks integrator (HIBI). This algorithm is motivated by the desire to integrate itembanks provided by publishers to the users of an e-learning system, which generally use different content structures. The proposed algorithm starts off with one of the itembanks as a reference itembank. All the items on the other itembanks are integrated into the reference itembank to create the so-called meta-itembank. Moreover, by treating the metaitembank thus created as the reference itembank, it can be easily extended by using exactly the same algorithm. We also use the concept of ontology to share itemsets with other systems. The experimental results showed that the proposed algorithm can provide an extremely high quality result in terms of both the relevance of items and the computation time in Chinese itemsets.

In this paper, we present an Intelligent Cluster Search Engine System, called ICSE. This system is motivated by the observation that traditional search engines present to the users a set of non-classified web pages based on its ranking mechanism, and the unfortunate results are that they usually can not satisfy the need of users. For this reason, ICSE provides to the user a set taxonomic web pages in response to a user's query, and thus it would greatly help the users filter out irrelevant or redundant information. The proposed system can be divided into two parts. The first is the knowledge base constructed by Open Directory Project and Yahoo! Directory. The second is the fast clustering algorithm described herein for clustering the web pages. In addition, in response to a user's query, the proposed system will first send the query to a meta-search engine. Then, it will create a clustered document set using the given knowledge base and the clustering algorithm of ICSE. Our simulation result showed that the proposed system can enhance the relevance and coverage of the search results that the users need compared with traditional search engines.

Most, if not all, of the researches in support vector machine (SVM) based face recognition algorithms have generally presumed that the classifier is static and thus unscalable, due to the fact that SVM is a supervised learning method. This paper introduces a novel SVM based face recognition method - by dynamically adding ¿new¿ faces of existing or new persons into the face database - which circumvents these difficulties. In other words, the proposed algorithm is able to learn and recognize faces that are not in the face database before. The paper presents the theory and the experimental results using the new approach. Our experimental results indicate that the accuracy rate of the proposed algorithm ranges from 91% up to 100% and outperforms all the others.

In this paper, we present a system for predicting the examination result, called automatic score prediction system (ASPS). ASPS builds on the lecture structure created by a teacher or a particular pedagogy a course content tree. Each examination sheet is then derived from this course content tree. Besides, each of the question items in an examination sheet belongs to either a section, a chapter, or a unit The difficulty and authority of the question items are determined by the weighted score. The main task of the system is in the classification of the testing results and in the provision of the learning curves and the remedial courses. The learning achievement and testing results can be summarized as a learning curve to the students. By analyzing the learning accomplishments, not only can ASPS predict the probabilistic result of the next examination, but it can also provide reviews or remedial lessons to the students. The main contributions of the ASPS system described herein are twofold: (1) the system can produce the learning curve based on the examination result, which would make it easy for the students and teachers to understand the learning results. (2) The system can provide the course contents to the students to help them review after the examination. Moreover, it provides suggestions for teaching on the not yet acquainted and confused contents.

Over the past decade, more and more users of the Internet rely on the search engines to help them find the information they need. However, the information they find depends, to a large extent, on the ranking mechanism of the search engines they use. Not surprisingly, it, in general, consists of a large amount of information that is completely irrelevant. To help users of the Internet find the information they are looking for quickly, an efficient algorithm for building the summaries of a collection of documents found by a search engine in response to a user query, called DISCO (Distribution Scoring) is proposed. To demonstrate the performance of the proposed algorithm, Reuters-21578 text categorization collection and some search results from Google are used in our simulation. Moreover, several metrics such as coverage, overlap, and the computation time are employed in evaluating the quality and quantity of the proposed algorithm. All our simulation results indicate that the proposed algorithm outperforms all the existing algorithms in terms of not only the usefulness of the summaries but also the running time.

In this paper, we present a simple but fast codebook generation algorithm, called PREGLA (pattern reduction enhanced GLA). The proposed algorithm is fundamentally different from the previous approaches in that the latter focuses on reducing the size of the codebook whereas the former focuses on using pattern reduction to reduce the computation time. The proposed algorithm is motivated by the observation that input vectors that are ldquostaticrdquo during the training process can be considered as part of the final solutions and thus can be compressed and removed to eliminate the redundant computations at the later iterations of the training process. To evaluate the performance of the proposed algorithm, we compare the proposed algorithm with ldquoefficientrdquo VQ algorithms published recently such as nearest partition set search, fast vector quantization algorithm, and standard GLA. Our experimental results indicate that the proposed algorithm can reduce the computation time from 27.86% up to about 80.45% compared to that of standard GLA and other fast GLA-based algorithms alone.

This paper presents a simple but efficient algorithm for enhancing the performance of GA or GA-based algorithms while retaining the diversity of the search directions. The proposed algorithm is motivated by the observation that some of the genes common to all the individuals during the evolution process can be considered as part of the final solutions and thus can be removed to eliminate the redundant computations at the later generations of the evolution process. To evaluate the performance of the proposed algorithm, we use it to solve the traveling salesman problem (TSP). The benchmarks for the TSP problem range in size from 574 up to 2,152 cities. For the three problems evaluated, our experimental results indicate that the proposed algorithm can reduce the computation time from 28% up to about 84% compared to that of traditional GA and GA-based algorithms alone.

In this paper, we present an efficient algorithm, called pattern reduction (PR) algorithm, to reduce the time required for data clustering based on iterative clustering algorithms. Conceptually similar to a lossy data compression scheme, this algorithm removes at each iteration those data patterns that are close to the centroid of a cluster or remain in the same cluster for a certain number of iterations in a row and are thus unlikely to be moved again from one cluster to another at later iterations by computing a new pattern to represent all the data patterns removed. Our simulation results - from 2 to 1,000 dimensions and 150 to 6,000,000 patterns - indicate that the proposed algorithm can reduce the computation time of k-means, genetic k-means algorithm (GKA) and k-means with genetic algorithm (KGA) from 10% up to about 80% and that for high dimensional data sets, it can even reduce the computation time for more than 70%.

This paper presents a new internet search engine system called document clustering for search engines (DCSE). This system focuses on overcoming the following challenges faced by search engines: (1) relevance of the search results in response to a user query and (2) information coverage. The DCSE system is based upon a meta-search engine that integrates information retrieval (IR), information extraction (IE), genetic algorithm (GA) and document clustering algorithm into a single system. DCSE utilizes information extraction techniques and vector space model (VSM) calculations to determine the relevance of various data, and then categorizes the data via information retrieval and document clustering algorithm in order to better refine the result. Users will receive information that has been calculated and sorted and web links that are ranked according to their relevance. The end result will reduce the amount of time that users spend filtering out irrelevant data.

This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler for warping. The method is a direct computation, which is fundamentally different from the iterative back-projection approaches proposed in previous work. This paper shows that image-warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also significantly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made visually by comparing the resulting images and those using bi-linear resampling and back-projection and quantitatively using OCR as a fundamental measure. The paper shows that even when the images are qualitatively similar, quantitative differences appear in machine processing.

Until now, all super-resolution algorithms have presumed that the images were taken under the same illumination conditions. This paper introduces a new approach to super-resolution, based on edge models and a local blur estimate, which circumvents these difficulties. The paper presents the theory and the experimental results using the new approach.

This paper introduces a new algorithm for enhancing image resolution from an image sequence. The approach we propose herein uses the integrating resampler proposed by M. Chiang and T. Boult (1996) as the underlying resampling algorithm. Moreover, it is a direct method, which is fundamentally different from the iterative, back-projection approaches proposed in previous work. We show that image warping techniques may have a strong impact on the quality of image resolution enhancement. By coupling the degradation model of the imaging system directly into the integrating resampler, we can better approximate the warping characteristics of real sensors, which also highly improve the quality of super-resolution images. Examples of super-resolutions are given for gray-scale images. Evaluations are made by comparing the resulting images and those using bi-linear resampling and back-projection. Results from our experiments show that integrating resampler outperforms traditional bi-linear resampling.