Optimization is an integral part to science and engineering. Most real-world applications involve complex optimization processes, which are difficult to solve without advanced computational tools. With the increasing challenges of fulfilling optimization goals of current applications there is a strong drive to advance the development of efficient optimizers. The challenges introduced by emerging problems include:

• objective functions which are prohibitively expensive to evaluate, so typically so only a small number of objective function evaluations can be made during the entire search,
• objective functions which are highly multimodal or discontinuous, and
• non-stationary problems which may change in time (dynamic).

This volume presents a collection of recent studies covering the spectrum of computational intelligence applications with emphasis on their application to challenging real-world problems. Topics covered include: Intelligent agent-based algorithms, Hybrid intelligent systems, Cognitive and evolutionary robotics, Knowledge-Based Engineering, fuzzy sets and systems, Bioinformatics and Bioengineering, Computational finance and Computational economics, Data mining, Machine learning, and Expert systems. "Computational Intelligence in Optimization" is a comprehensive reference for researchers, practitioners and advanced-level students interested in both the theory and practice of using computational intelligence in real-world applications.

Ever thought of using the time-tested tactics and techniques of the ancient ninja to understand the mind of today's ninja, the hacker? As a penetration tester or security consultant you no doubt perform tests both externally and internally for your clients that include both physical and technical tests. Throw traditional pen testing methods out the window for now and see how thinking and acting like a ninja can actually grant you quicker and more complete access to a company's assets. Get in before the hacker does with these unorthodox techniques. Use all of the tools that the ninja has: disguise, espionage, stealth, and concealment. Learn how to benefit from these tools by laying your plans, impersonating employees, infiltrating via alarm system evasion, discovering weak points and timing, spyware and keylogging software, and log manipulation and logic bombs. And, really, don't you want to be a ninja for a day just because they're cool? Let this book be your excuse!

• Discusses techniques used by malicious attackers in real-world situations
• Details unorthodox penetration testing techniques by getting inside the mind of a ninja
• Expands upon current penetration testing methodologies including new tactics for hardware and physical attacks

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait – often proximity according to some defined distance measure.

The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data, but it can be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems.

This 2nd edition is an update of the book "Wavelet Theory and Its Application to Pattern Recognition" published in 2000. Three new chapters, which are research results conducted during 2001-2008, will be added. The book consists of two parts - the first contains the basic theory of wavelet analysis and the second includes applications of wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition.

• Continuous Wavelet Transforms
• Multiresolution Analysis and Wavelet Bases
• Some Typical Wavelet Bases
• Step-Edge Detection by Wavelet Transform
• Characterization of Dirac-Edges with Quadratic Spline Wavelet Transform
• Construction of New Wavelet Function and Application to Curve Analysis
• Skeletonization of Ribbon-like Shapes with New Wavelet Function
• Feature Extraction by Wavelet Sub-Patterns and Divider Dimensions
• Document Analysis by Reference Line Detection with 2-D Wavelet Transform
• Chinese Character Processing with B-Spline Wavelet Transform
• Classifier Design Based on Orthogonal Wavelet Series

Data mining and data modeling are hot topics and are under fast development. Because of its wide applications and rich research contents, a lot of practitioners and academics are attracted to work on these areas. In the view of promoting the communications and collaborations among the practitioners and researchers in Hong Kong, a two-day workshop on data mining and modeling was held on 27-28 June 2002. Prof. Ngaiming Mok, the Director of the Institute of Mathematical Research, The University of Hong Kong and Prof. Tze Leung Lai, Stanford University and the C.V. Starr Professor of the University of Hong Kong initialized the workshop. The workshop was organized by Dr. Michael Kwok-Po Ng, Department of Mathematics, The University of Hong Kong, and supported by the Institute of Mathematical Research and Hong Kong Mathematical Society. The two-day workshop is the first workshop on data mining and modeling in Hong Kong. It aims at promoting research interest in mathematical, statistical and computational methods and models in data mining for computer scientists, mathematicians, engineers and statisticians to foster contacts and inter-flow.

This book contains selected papers presented in the workshop. The papers fall into two main categories: data mining and data modeling. Data mining papers contain pattern discovery, clustering algorithms, classification and practical applications in stock market. Data modeling papers deal with neural network models, time series models, statistical models and practical applications. In the following, we give brief summaries for individual paper.

The problem of mining frequent sequences is to extract frequently occurring subsequences in a sequence database. Many algorithms have been proposed to solve the problem efficiently. Kao and Zhang survey several notable algorithms for mining frequent sequences, and analyze their characteristics.

Feng et al. discuss the gene selection problem which is an important issue in microarray data analysis and has critical implications for the discovery of genes related to serious diseases. They propose a Fisher optimization model in gene selection and uses Fisher linear discriminant in classification. They also demonstrated the validity of this method by using public data.

Computer and Information Sciences is a unique and comprehensive review of advanced technology and research in the field of Information Technology. It provides an up to date snapshot of research in Europe and the Far East (Hong Kong, Japan and China) in the most active areas of information technology, including Computer Vision, Data Engineering, Web Engineering, Internet Technologies, Bio-Informatics and System Performance Evaluation Methodologies.

In this paper we are adding partial persistence to a balanced search tree with a worst case constant update time [6] (in the case that the position of the update is given), via the node-copying method [5].

The idea in [6] is to organize the leaves of an (a, b) tree into buckets, with each bucket containing O(h) leaves, where h is the height of tree. A brief description of the structure is as follows: In every bucket, a pointer (called r_pointer) is stored, that points to an ancestor (or to a node “near” an ancestor) of the bucket. When an update occurs inside the bucket, we follow the r_pointer, rebalance the pointed ancestor and set the r_pointer to point one level upwards. After each such step, the bucket is split incrementally and, when the r_pointer reaches the root of the tree, the incremental process completes. Let u be the node pointed by the r_pointer of a bucket. To rebalance u, the following actions are performed: If u has more than b children (we call such a node big), u is split into two small nodes otherwise u is left intact (we call such a node small). In either case, the r_pointer is moved up one level. It is proved in [6] that, starting from an (α, b)-tree, this algorithm produces an (α, 2b)-tree.

This book is about relations between three different areas of mathematics and theoretical computer science: combinatorial group theory, cryptography, and complexity theory. It is explored how non-commutative (infinite) groups, which are typically studied in combinatorial group theory, can be used in public key cryptography. It is also shown that there is a remarkable feedback from cryptography to combinatorial group theory because some of the problems motivated by cryptography appear to be new to group theory, and they open many interesting research avenues within group theory.

Then, complexity theory, notably generic-case complexity of algorithms, is employed for cryptanalysis of various cryptographic protocols based on infinite groups, and the ideas and machinery from the theory of generic-case complexity are used to study asymptotically dominant properties of some infinite groups that have been applied in public key cryptography so far.

Its elementary exposition makes the book accessible to graduate as well as undergraduate students in mathematics or computer science.