Advances in Fuzzy Method for Natural Computing
Both natural computing and fuzzy method exhibit human-like mental processing. Natural computing is the field of research that works with computational techniques inspired in part by natural systems. Fuzzy method is an alternative to traditional notions of set membership and logic, and it occurs in many applications of engineering, science and medicine. This paper presents a survey of recent advances in fuzzy method for natural computing, such as fuzzy artificial neural networks, fuzzy genetic algorithms, fuzzy ant colony optimization, fuzzy artificial immune systems, etc. We utilize uncertainty handling capacity of fuzzy method for modeling ambiguity, and the learning ability of natural computing for efficiently traversing large search spaces.

title: Interweave Neural Networks with Evolutionary Algorithms, Cellular Computing, Bayesian Learning and Ensemble Learning
abstract: A neural networks are able to give solutions to complex problems in business intelligence and financial engineering due to their nonlinear processing. This paper consists of a survey of various business intelligence and financial engineering and so on applications based on the neural networks, and also a summary of the recent techniques such as still evolutionary algorithms, cellular computing, Bayesian learning and ensemble learning. The neural networks can be powerful tools in several fields of business intelligence and financial engineering. It can be concluded that, the future of neural networks lies in their use in conjunction with other advanced technologies.

title: The transient solution of time-dependent M/M/1 queues
abstract: The transient behavior of time-dependent M/M/1 queues is studied. The boundary probability function pi /sub 0/(t), which is the probability that the queue is empty at time t, is shown with analyticity arguments to satisfy a Volterra-type integral equation. The boundary integral equation is derived, and a numerical algorithm is used to solve the integral equation and to find the expected queue size from pi /sub 0/(t). The approach can be applied to many other types of time-dependent queues. Examples are given.

title: Generalized Semi-Infinite Optimization and its Application in Robotics' Path Planning Problem

title: Evolutionary Algorithm Based on Overlapped Gene Expression
abstract: Inspired by the overlap gene expression in biological study, this paper proposes a novel evolutionary algorithm-EAOGE i.e. Evolutionary Algorithm based on Overlapped Gene Expression. Different from existing works, EAOGE suggests a new expression structure of genes with probabilities of overlapped expression for some segments. The main contributions are: (1) Proposing a novel model and an algorithm of gene expression while borrowing some ideas from artificial immunity algorithm; (2) Analyzing the expressing space and encode characteristic of the new model; (3) The extensive experiments in function finding shows that new model is 2.8~9.7 times faster than usual GEP method, and in higher-degree polynomial function finding, the success rate of EAOGE is over 10 times than usual GEP.

title: Intelligence Computing Methods in Electronic Commerce and Security
abstract: This paper consists of a survey of various e-business, e-marketplaces, graphics and imaging processing, pattern recognition, computer vision, access control, authentication, authorization, etc. applications based on the intelligent computing, and also a summary of the recent techniques such as still artificial neural networks, swarm intelligence, artificial immune systems, fractal geometry, artificial life, ant colony algorithm, genetic algorithm, evolutionary algorithm, quantum computing. Intelligent computing can be powerful tools in several fields of electronic commerce and security.

title: An Improved Fuzzy Image Enhancement Algorithm
abstract: An effective method for fuzzy image enhancement was presented by Russo, which was controlled by tuning of one parameter. The fixed parameter was used for the entire image resulting in over blurring or sharpening of image features in some parts of the image. On the basis of these, in this paper, we applied Russo's algorithm on different kinds of noise and proposed an efficient method to obtain the parameter values adaptively. Firstly, each pixel went through a phase of smoothing and then followed by a phase of combining smoothing and sharpening; Secondly, to effectively enhance image, each pixel local feature was adaptively assigned different parameter values by evaluating pixel local features through a fuzzy membership function; Finally, compared with Russopsilas method and other methods, experimental results indicated that the proposed method could achieve better performance than traditional methods for the enhancement of images corrupted with impulse and Gaussian noise.