An Evolutionary Approach to the Design of Controllable Cellular Automata Structure for Random Number Generation

Cellular Automata (CA) has been used in pseudorandom number generation over a decade. Recent studies show that two-dimensional (2-d) CA Pseudorandom Number Generators (PRNGs) may generate better random sequences than conventional one-dimensional (1-d) CA PRNGs, but they are more complex to implement in hardware than 1-d CA PRNGs. In this paper, we propose a new class of 1-d CA Controllable Cellular Automata (CCA) without much deviation from the structure simplicity of conventional 1-d CA. We give a general definition of CCA first and then introduce two types of CCA – CCA0 and CCA2. Our initial study on them shows that these two CCA PRNGs have better randomness quality than conventional 1-d CA PRNGs but their randomness is affected by their structures. To find good CCA0/CCA2 structures for pseudorandom number generation, we evolve them using the Evolutionary Multi-Objective Optimization (EMOO) techniques. Three different algorithms are presented in this paper. One makes use of an aggregation function; the other two are based on the Vector Evaluated Genetic Algorithm (VEGA). Evolution results show that these three algorithms all perform well. Applying a set of randomness tests on the evolved CCA PRNGs, we demonstrate that their randomness is better than that of 1-d CA PRNGs and can be comparable to that of two-dimensional CA PRNGs

Layered cellular automata for pseudorandom number generation

The proposed Layered Cellular Automata (L-LCA), which comprises of a main CA with L additional layers of memory registers, has simple local interconnections and high operating speed. The time-varying L-LCA transformation at each clock can be reduced to a single transformation in the set formed by the transformation matrix of a maximum length Cellular Automata (CA), and the entire transformation sequence for a single period can be obtained. The analysis for the period characteristics of state sequences is simplified by analyzing representative transformation sequences determined by the phase difference between the initial states for each layer. The L-LCA model can be extended by adding more layers of memory or through the use of a larger main CA based on widely available maximum length CA. Several L-LCA (L=1,2,3,4) with 10- to 48-bit main CA are subjected to the DIEHARD test suite and better results are obtained over other CA designs reported in the literature. The experiments are repeated using the well-known nonlinear functions and in place of the linear function used in the L-LCA. Linear complexity is significantly increased when or is used

Editorial -Special issue on adaptive multimedia computing

In recent years, there is an emerging research area in multimedia computing, with the increasing number of related work in scalable video, adaptive multimedia documents, adaptive multimedia services, to name just a few. This new trend comes about partly due to the increasing use of mobile media devices where media requirements could change among users and devices and at different times of reception or presentation, and partly due to the changing network conditions, where best-effort service is the general practice. Any change in Quality of Services (QoS) could imply a change in the delivery or scheduling of media contents. To complicate the matter, user interruptions or requirement changes during the communication process could also occur; for example, a user may not be satisfied with the current media quality and decide an upgrade in real time. The status quo is that this new research paradigm is beginning to take shape while no effort has been made to draw a roadmap for it. We could see some major research work missing, for example, formal methods or modeling of adaptive multimedi

Modeling interactive memex-like applications based on self-modifiable petri nets

This paper introduces an interactive Memex-like application using a self-modifiable Petri Net model – Self-modifiable Color Petri Net (SCPN). The Memex (“memory extender”) device proposed by Vannevar Bush in 1945 focused on the problems of “locating relevant information in the published records and recording how that information is intellectually connected.” The important features of Memex include associative indexing and retrieval. In this paper, the self-modifiable functions of SCPN are used to achieve trail recording and retrieval. A place in SCPN represents a website and an arc indicates the trail direction. Each time when a new website is visited, a place corresponding to this website will be added. After a trail is built, users can use it to retrieve the websites they have visited. Besides, useful user interactions are supported by SCPN to achieve Memex functions. The types of user interactions include: forward, backward, history, search, etc. A simulator has been built to demonstrate that the SCPN model can realize Memex functions. Petri net instances can be designed to model trail record, back, and forward operations using this simulator. Furthermore, a client-server based application system has been built. Using this system, a user can surf online and record his surfing history on the server according to different topics and share them with other users

Ontology acquisition and exchange of evolutionary product-brokering agents

Agent-based electronic commerce (e-commerce) has been booming with the development of the Internet and agent technologies. However, little effort has been devoted to exploring the learning and evolving capabilities of software agents. This paper addresses issues of evolving software agents in e-commerce applications. An agent structure with evolution features is proposed with a focus on internal hierarchical knowledge. We argue that knowledge base of agents should be the cornerstone for their evolution capabilities, and agents can enhance their knowledge bases by exchanging knowledge with other agents. In this paper, product ontology is chosen as an instance of knowledge base. We propose a new approach to facilitate ontology exchange among e-commerce agents. The ontology exchange model and its formalities are elaborated. Product-brokering agents have been designed and implemented, which accomplish the ontology exchange process from request to integration

Self-modifiable color petri nets for modeling user manipulation and network event handling

A Self-Modifiable Color Petri Net (SMCPN) which has multimedia synchronization capability and the ability to model user manipulation and network event (i.e. network congestion, etc.) handling is proposed in this paper. In SMCPN, there are two types of tokens: resource tokens representing resources to be presented and color tokens with two sub-types: one associated with some commands to modify the net mechanism in operation, another associated with a number to decide iteration times. Also introduced is a new type of resource token named reverse token that moves to the opposite direction of arcs. When user manipulation/network event occurs, color tokens associated with the corresponding interrupt handling commands will be injected into places that contain resource tokens. These commands are then executed to handle the user manipulation/network event. SMCPN has the desired general programmability in the following sense: 1) It allows handling of user manipulations or pre-specified events at any time while keeping the Petri net design simple and easy. 2) It allows the user to customize event handling beforehand. This means the system being modeled can handle not only commonly seen user interrupts (e.g. skip, reverse, freeze), the user is free to define new operations including network event handling. 3) It has the power to simulate self-modifying protocols. A simulator has been built to demonstrate the feasibility of SMCPN

Recursive Percentage based Hybrid Pattern Training for Supervised Learning

Supervised learning algorithms, often used to find the I/O relationship in data, have the tendency to be trapped in local optima as opposed to the desirable global optima. In this paper, we discuss the RPHP learning algorithm. The algorithm uses Real Coded Genetic Algorithm based global and local searches to find a set of pseudo global optimal solutions. Each pseudo global optimum is a local optimal solution from the point of view of all the patterns but globally optimal from the point of view of a subset of patterns. Together with RPHP, a Kth nearest neighbor algorithm is used as a second level pattern distributor to solve a test pattern. We also show theoretically the condition under which finding several pseudo global optimal solutions requires a shorter training time than finding a single global optimal solution. As the difficulty of curve fitting problems is easily estimated, we verify the capability of the RPHP algorithm against them and compare the RPHP algorithm with three counterparts to show the benefits of hybrid learning and active recursive subset selection. The RPHP shows a clear superiority in performance. We conclude our paper by identifying possible loopholes in the RPHP algorithm and proposing possible solutions

Class decomposition for GA-based classifier agents – A Pitt approach

Incremental learning has been widely addressed in the machine learning literature to cope with learning tasks where the learning environment is ever changing or training samples become available over time. However, most research work explores incremental learning with statistical algorithms or neural networks, rather than evolutionary algorithms. The work in this paper employs genetic algorithms (GAs) as basic learning algorithms for incremental learning within one or more classifier agents in a multi-agent environment. Four new approaches with different initialization schemes are proposed. They keep the old solutions and use an “integration” operation to integrate them with new elements to accommodate new attributes, while biased mutation and crossover operations are adopted to further evolve a reinforced solution. The simulation results on benchmark classification data sets show that the proposed approaches can deal with the arrival of new input attributes and integrate them with the original input space. It is also shown that the proposed approaches can be successfully used for incremental learning and improve classification rates as compared to the retraining GA. Possible applications for continuous incremental training and feature selection are also discussed

A multi-agent architecture for electronic payment

The Internet has brought about innumerable changes to the way enterprises do business. An essential problem to be solved before the widespread commercial use of the Internet is to provide a trustworthy solution for electronic payment. We propose a multi-agent mediated electronic payment architecture in this paper. It is aimed at providing an agent-based approach to accommodate multiple e-payment schemes. Through a layered design of the payment structure and a well-defined uniform payment interface, the architecture shows good scalability. When a new e-payment scheme or implementation is available, it can be plugged into the framework easily. In addition, we construct a framework allowing multiple agents to work cooperatively to realize automation of electronic payment. A prototype has been built to illustrate the functionality of this design. Finally we discuss the security issues

A golden template self-generating method for patterned wafer inspection

This paper presents a novel golden template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both directions. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find out possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented