18388949
A probabilistic cooperative-competitive hierarchical model for global optimization
Abstract Stochastic searching methods have been applied widely to areas such as continuous and combinatorial optimization problems in a number of disciplines. Many existing methods solve these problems by navigating on the surface of the possibly rugged landscape. This kind of navigation is not very effective because the property of the landscape at different resolutions can be very different. Time spent at the beginning of the search on the detailed part of the landscape is often useless. Appropriate searching strategies should be adopted at different resolutions. In this paper, we propose a new probabilistic searching model for global optimization. The main contributions of the model are (1) to provide a basis for resolution control and smoothing of search space and (2) to introduce continuous memory into stochastic search. The basis of resolution control is achieved by dividing the search space into a finite number of n -dimensional partitions structurally. The number of partitions governs the resolution of the search space. The more the partitions, the finer is the search space and the more detailed and rugged is the landscape. The benefits are twofold. First, the rugged landscape problem can be smoothed, because the ruggedness is a matter of the number of partitions. Hence, the difficulty in search due to the ruggedness of the landscape can be controlled. Second, it provides a basis to implement algorithms that may change the ‘view’ of the landscape during the search process because we can dynamically divide the search space accordingly. Another important feature that we use is continuous memory . Throughout the search process, searching experience is continuously accumulated in order to shape the global picture of the search space guiding the future searching direction. We present results on the algorithm performance in handling numerical function optimization. The empirical results show that our new model is comparable to, and in many cases performs better than, that of the other advanced methods in terms of solution quality and computation required.
18141920
Learning functional dependency networks based on genetic programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle discrete values only; it cannot handle continuous, interval and ordinal ones, which must be converted to discrete values and the order information is lost. Thus, BN tends to have higher network complexity and lower understandability. In this paper, we present a novel dependency network which can handle discrete, continuous, interval and ordinal values through functions; it has lower network complexity and stronger expressive power; it can represent any kind of relationships; and it can incorporate a-priori knowledge though user-defined functions. We also propose a novel Genetic Programming (GP) to learn dependency networks. The novel GP does not use any knowledge-guided nor application-oriented operator, thus it is robust and easy to replicate. The experimental results demonstrate that the novel GP can successfully discover the target novel dependency networks, which have the highest accuracy and the lowest network complexity.
5226759
Genetic Parallel Programming: Design and Implementation
This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential programif required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.
17795196
Scalable model-based clustering by working on data summaries
The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources. We present a two-phase scalable model-based clustering framework: first, a large data set is summed up into subclusters; Then, clusters are directly generated from the summary statistics of subclusters by a specifically designed expectation-maximization (EM) algorithm. Taking example for Gaussian mixture models, we establish a provably convergent EM algorithm, EMADS, which embodies cardinality, mean, and covariance information of each subcluster explicitly. Combining with different data summarization procedures, EMADS is used to construct two clustering systems: gEMADS and bEMADS. The experimental results demonstrate that they run several orders of magnitude faster than the classic EM algorithm with little loss of accuracy. They generate significantly better results than other model-based clustering systems using similar computational resources.
18293423
Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks
We have developed a new approach to learning Bayesian network structures based on the minimum description length (MDL) principle and evolutionary programming. It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.
15319623
Inducing Logic Programs With Genetic Algorithms: The Genetic Logic Programming System
The Genetic Logic Programming System (GLPS) combines the learning power of genetic algorithms and the knowledge representation power of logic programming to overcome the shortcomings of existing paradigms. >
14053268
Data Synchronization Methods Based on ShuffleNet and Hypercube for Networked Information Systems
In contrast to a typical single source of data updates in Internet applications, data files in a networked information system are often distributed, replicated, accessed and updated by multiple nodes. Due to concurrent updates, replicated data files must be synchronized. For certain applications, stringent concurrency control must be employed to ensure data integrity, while for other applications, periodic data synchronization may enable very efficient data sharing. For the latter applications, this paper devises the ShuffleNet and hypercube schemes for data synchronization in such networked information systems. Their performance in terms of update delay, processing complexity, failure tolerance and growth complexity is examined. Our results reveal that the ShuffleNet and hypercube scheme provide identical maximum update delay and similar processing complexity. However, as the number of nodes in the system changes (e.g., due to failure or temporary out of service for maintenance), the hypercube scheme maintains all existing synchronization sessions and greatly simplifies system administration overhead such as moving files from node to node for the purpose of data synchronization. The ShuffleNet scheme does provide a higher degree of failure tolerance for global data files, but the hypercube scheme provides more than adequate failure tolerance. Lastly, a generalization of the hypercube scheme, based on the ideas of shift registers, is also proposed for systems where the number of nodes is a perfect power of 2.
19717563
The Use of ICT in the Delivery of Online Services
This book presents the results of an International Federation for Information Processing (IFIP) working conference, held December 2004 in Melbourne, Australia
320235
Handling signature purposes in workflow systems
Abstract In paper-based workflow systems, signatures of individuals or groups of people have been used extensively for different purposes. Currently there are numerous studies on computerizing workflow systems. Also there are studies on implementing digital signatures in electronic media. But the diversified purposes of a signature in workflow makes a straightforward implementation of digital signature schemes inadequate. There are few studies on the implication of different signature purposes on electronic workflow systems. The purposes of signature are closely associated with the two modes of decision making, namely single and group. These two modes of decision making in turn lead to single and group signatures. This paper reports our comprehensive studies on signature purposes. These include the analysis of common signature purposes in workflow, classification of these purposes, classification of modes of decision making associated with these signature purposes, signing and validation requirements for handling these signature purposes, and finally an architecture to be incorporated in workflow engines for handling these signature purposes. In summary, this paper addresses a commonly neglected problem in information systems research: management control by signature in workflow.
43168296
Traffic models for wireless communication networks
The authors introduce a deterministic fluid model and two stochastic traffic models for wireless networks. The setting is a highway with multiple entrances and exits. Vehicles are classified as calling or non-calling, depending on whether they have calls in progress. The deterministic model ignores the behavior of individual vehicles and treats them as a continuous fluid, whereas the stochastic traffic models consider the random behavior of each vehicle. However, all three model's use the same two coupled partial (or ordinary) differential equations to describe the system evolution. The call density and call handoff rate (or their expected values in the stochastic models) are readily computable by solving these equations. Numerical examples are presented to illustrate how the models can be used to investigate various aspects of time and space dynamics in wireless networks. These examples also show that the models can serve as useful tools for system engineering and planning.<<ETX>>
18463934
Design of a 1.5-V high-order curvature-compensated CMOS bandgap reference
A low-voltage low-noise high-order curvature-compensated CMOS bandgap reference is proposed in this paper. Two resistor strings are added to the bandgap circuit to reduce the supply voltage. In addition, a temperature-dependent resistor ratio generated by a high-resistive poly resistor is used to provide the high-order compensation. The proposed bandgap voltage reference can operate down to a 1.5V supply (where V/sub th/ = 0.9V at 0/spl deg/C) with a temperature coefficient of less than 15.2ppm//spl deg/C and an average line regulation of 5.5mV/V.