Estimation of Distribution Algorithms for the Machine-Part Cell Formation
The machine-part cell formation is a NP- complete combinational optimization in cellular manufacturing system. Previous researches have revealed that although the genetic algorithm (GA) can get high quality solutions, special selection strategy, crossover and mutation operators as well as the parameters must be defined previously to solve the problem efficiently and flexibly. The Estimation of Distribution Algorithms (EDAs) has recently been recognized as a new computing paradigm in evolutionary computation which can overcome some drawbacks of the traditional GA mentioned above. In this paper, two kinds of the EDAs, UMDA and EBNA BIC are applied to solve the machine-part cell formation problem. Simulation results on six well known problems show that the UMDA and EBNA BIC can attain satisfied solutions more simply and efficiently.

title: Neural Network Identification Method Applied to the Nonlinear System
abstract: A kind of neural network solution method has been proposed in the paper aiming at a class of non-linear process control system with the characteristic of time delay. In this scheme, a new-type associative memory neural network is used to model the controlled system, and the fuzzy neural network with inverse identification structure is adopted to control the nonlinear process system. This fuzzy neural network control method adopts the structure of three layers combine neural network identifier with inverse structure. Computer simulation and lab application show that it is effective to adopt this scheme to control on-linear process system with time delay.

title: Hybrid Ant Colony Algorithm and Its Application on Function Optimization
abstract: A new hybrid ant colony algorithm was proposed. Firstly, weight factor was introduced to the binary ant colony algorithm, and then we obtained a new probability by combining probability model of Population based incremental learning (PBIL) with transfer probability of ants pheromone . The new population are produced by probability model of PBIL, transfer probability of ants pheromone and the probability of proposed algorithm so that population polymorphism is ensured and the optimal convergence rate and the ability of breaking away from the local minima are improved. Optimization simulation results based on the benchmark test functions show that the hybrid algorithm has higher convergence rate and stability than binary ant colony algorithm (BACA) and Population based incremental learning (PBIL).

title: A Population-Based Incremental Learning Algorithm with Elitist Strategy
abstract: The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.

title: Adding the temporal dimension to search - a case study in publication search
abstract: The most well known search techniques are perhaps the PageRank and HITS algorithms. In this paper, we argue that these algorithms miss an important dimension, the temporal dimension. Quality pages in the past may not be quality pages now or in the future. These techniques favor older pages because these pages have many in-links accumulated over time. New pages, which may be of high quality, have few or no in-links and are left behind. Research publication search has the same problem. If we use the PageRank or HITS algorithm, those older or classic papers are ranked high due to the large number of citations that they received in the past. This paper studies the temporal dimension of search in the context of research publication. A number of methods are proposed to deal with the problem based on analyzing the behavior history and the source of each publication. These methods are evaluated empirically. Our results show that they are highly effective.

title: Hybrid Algorithm Combining Ant Colony Algorithm with Genetic Algorithm for Continuous Domain
abstract: Ant colony algorithm is a kind of new heuristic biological modeling method which has the ability of parallel processing and global searching. By use of the properties of ant colony algorithm and genetic algorithm, the hybrid algorithm which adopts genetic algorithm to distribute the original pheromone is proposed to solve the continuous optimization problem. Several solutions are obtained using the ant colony algorithm through pheromone accumulation and renewal. Finally, by using crossover and mutation operation of genetic algorithm, some effective solutions are obtained. The results of experiments show better performances of the new algorithm based on six continuous test functions compared with the methods available in literature.

title: Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression
abstract: The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labeling all unlabeled examples as negative and use a linear function to learn from the noisy examples. To learn a linear function with noise, we perform logistic regression after weighting the examples to handle noise rates of greater than a half. With appropriate regularization, the cost function of the logistic regression problem is convex, allowing the problem to be solved efficiently. We also propose a performance measure that can be estimated from positive and unlabeled examples for evaluating retrieval performance. The measure, which is proportional to the product of precision and recall, can be used with a validation set to select regularization parameters for logistic regression. Experiments on a text classification corpus show that the methods proposed are effective.

title: An Effective Approach for Hiding Sensitive Knowledge in Data Publishing
abstract: Recent efforts have been made to address the problem of privacy preservation in data publishing. However, they mainly focus on preserving data privacy. In this paper, we address another aspect of privacy preservation in data publishing, where some of the knowledge implied by a dataset are regarded as private or sensitive information. In particular, we consider that the data are stored in a transaction database, and the knowledge is represented in the form of patterns. We present a data sanitization algorithm, called SanDB, for effectively protecting a set of sensitive patterns, meanwhile attempting to minimize the impact of data sanitization on the non-sensitive patterns. The experimental results show that SanDB can achieve significant improvement over the best approach presented in the literature.

title: JOINT ESTIMATION OF IMAGE AND COIL SENSITIVITIES IN PARALLEL SPIRAL MRI
abstract: Spiral MRI has received increasing attention due to its reduced T 2*-decay and robustness against bulk physiologic motion. In parallel imaging, spiral trajectories are especially of great interest due to their inherent self-calibration capabilities, which is especially useful for dynamic imaging applications such as fMRI and cardiac imaging. The existing self-calibration techniques for spiral use the k-space center data that are sampled densely in the accelerated acquisition for coil sensitivity estimation. There exists a trade-off in choosing the radius of the center data: it must be sufficiently large to contain all major spatial frequencies of coil sensitivity, but not too large to cause significant aliasing artifacts due to undersampling below Nyquist rate as the trajectory moves away from the center k-space. To address this tradeoff, we generalize the JSENSE approach, which has demonstrated success in Cartesian case, to spiral trajectory. Specifically, the method jointly estimates the coil sensitivities and reconstructs the desired image through cross validations so that the sensitivities are estimated from the full data recovered by SENSE instead of the center k-space data only, thereby increasing high frequency information without introducing aliasing artifacts. We use experimental results to show the proposed method improves sensitivities, which leads to a more accurate SENSE reconstruction