The Dynamics of the Electronic Market: An Evolutionary Game Approach

The capabilities afforded by network technologies have facilitated the growth of electronic commerce. However, online frauds pose serious challenges to the further adoption of the electronic market. In order to promote trust and reduce transaction risks, various trusted third parties have emerged and new models have been proposed. Will people use the trusted third parties while conducting online transactions? How will the electronic market evolve? This research attempts to identify the different equilibria of the electronic market using an evolutionary game theoretic approach and to explore the best strategy to do transactions in the electronic market. Also, the work provides a theoretical justification to the emergence and necessity of trusted third parties for electronic transactions.     

An Evolutionary Approach Toward Dynamic Self-Generated Fuzzy Inference Systems

An evolutionary approach toward automatic generation of fuzzy inference systems (FISs), termed evolutionary dynamic self-generated fuzzy inference systems (EDSGFISs), is proposed in this paper. The structure and parameters of an FIS are generated through reinforcement learning, whereas an action set for training the consequents of the FIS is evolved via genetic algorithms (GAs). The proposed EDSGFIS algorithm can automatically create, delete, and adjust fuzzy rules according to the performance of the entire system, as well as evaluation of individual fuzzy rules. Simulation studies on a wall-following task by a mobile robot show that the proposed EDSGFIS approach is superior to other related methods.      

An Integrative Economic Optimization Approach to Systems Development Risk Management

Despite significant research progress on the problem of managing systems development risk, we are yet to see this problem addressed from an economic optimization perspective. Doing so entails answering the question: What mitigations should be planned and deployed throughout the life of a systems development project in order to control risk and maximize project value? We introduce an integrative economic optimization approach to solving this problem. The approach is integrative since it bridges two complementary research streams: one takes a traditional microlevel technical view on the software development endeavor alone, another takes a macrolevel business view on the entire life cycle of a systems project. Bridging these views requires recognizing explicitly that value-based risk management decisions pertaining to one level impact and can be impacted by decisions pertaining to the other level. The economic optimization orientation follows from reliance on real options theory in modeling risk management decisions within a dynamic stochastic optimization setting. Real options theory is well suited to formalizing the impacts of risk as well as the asymmetric and contingent economic benefits of mitigations, in a way that enables their optimal balancing. We also illustrate how the approach is applied in practice to a small realistic example.     

An Evolutionary Approach to Multiobjective Clustering

The framework of multiobjective optimization is used to tackle the unsupervised learning problem, data clustering, following a formulation first proposed in the statistics literature. The conceptual advantages of the multiobjective formulation are discussed and an evolutionary approach to the problem is developed. The resulting algorithm, multiobjective clustering with automatic k-determination, is compared with a number of well-established single-objective clustering algorithms, a modern ensemble technique, and two methods of model selection. The experiments demonstrate that the conceptual advantages of multiobjective clustering translate into practical and scalable performance benefits     

Contribution to Design of Complex Mechatronic Systems. An Approach through Evolutionary Optimization

This paper describes a new evolutionary methodology aimed at optimizing various and heterogeneous data in common evolution. The representation of solutions uses mixed-integer genotypes and variable-length chromosomes to face a complex problem of task decomposition and high-level control generation. A memory operator is introduced to face convergency uncertainties issued from the irregularities of both discontinuous evaluation function and heterogeneous solution representation. The stability of the evolutionary algorithm is analyzed with dimension changes in the optimization problem.     

Fail-Awareness: An Approach to Construct Fail-Safe Systems

We present a framework for building fail-safe hard real-time applications in timed asynchronous distributed systems subject to communication partitions and performance, omission, and crash failures. Most distributed systems built from commercial-off-the-shelf (COTS) processor and communication services are subject to such partitions because their COTS components do not provide hard real-time guarantees. Also custom designed systems can be subject to partitions due to unmaskable link or router failures. The basic assumption behind our approach is that each processor has a local hardware clock that proceeds within a linear envelope of real-time. This allows one to compute an upper bound on the actual delays incurred by a particular processing sequence or message transmission. Services and applications can use these computed bounds to detect when they cannot guarantee all their standard properties because of excessive delays. This allows an application to be fail-aware, that is, to detect when it cannot guarantee all its safety properties and in particular, to detect when to switch to a fail-safe mode.     

Application of Evolutionary Strategies to Optimal Design of Multibody Systems

This paper is concerned with the application of evolutionary strategiesto the optimization of the kinematic or dynamic behaviour of mechanicalsystems. Although slower than classical deterministic, gradient-basedmethods, they represent an interesting alternative: they are global,they should be more robust as they do not rely on continuity andderivability conditions and they can use the simulation software as is.They are inspired from natural evolution: the design variablescorresponding to the genes of an individual mutate from generation togeneration and the ones who survive are those that are the best fittedto their environment, that's to say with the best objective function.The implementation of evolutionary strategies is presented as well assome guidelines to choose the most important parameters. Two examplesare developed for the sake of illustration: the kinematic optimizationof a suspension and the dynamic optimization of the comfort of a railwayvehicle. Finally, the performances are compared with respect todeterministic methods and genetic algorithms.     

An Evolutionary Squeaky Wheel Optimization Approach to Personnel Scheduling

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyze a technique called Evolutionary Squeaky Wheel Optimization and apply it to two different personnel scheduling problems. Evolutionary Squeaky Wheel Optimization improves the original squeaky wheel optimization's effectiveness and execution speed by incorporating two additional steps (selection and mutation) for added evolution. In the evolutionary squeaky wheel optimization, a cycle of analysis-selection-mutation-prioritization-construction continues until stopping conditions are reached. The aim of the analysis step is to identify below average solution components by calculating a fitness value for all components. The selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the prioritization step to first produce priorities that determine an order by which the following construction step then schedules the remaining components. Therefore, improvements in the evolutionary squeaky wheel optimization is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.     

An Approach to Evaluating E-Business Information Systems Projects

Capital investments in the e-business infrastructure add complexity to the IT payoff question because e-business interorganizational investments are deployed across multiple platforms, projects, vendors and partners. Traditional MIS research has been devoted to measuring the payoff from information technology (IT) investment without any specific focus on e-business. Flaws in the mature MIS performance measures seem to yield weak guidance for managers when evaluating IT success, thus diluting the potential for these measures to be adapted for use in the new e-business environment. A review of the conventional MIS payoff literature indicates that these measures may be incomplete, inaccurate, or inefficient for application to electronic commerce investments. This paper brings four new points to the e-business IT investment evaluation dialogue: (1) we first explore performance measurement validity flaws in our long established measures; (2) the paper discusses a two-by-two matrix delineating the gap between the quantitative versus qualitative performance measures of management information systems (MIS) initiatives; (3) sample e-business payoff techniques are discussed and reviewed in light of these historical imperfections, and (4) fourth, the paper proposes and describes an innovative framework derived from production theory economics for future research in evaluating e-business MIS implementations.     

IDS自适应特征选择算法——进化包装(Wrapper)算法分析

随着网络技术和网络规模的不断发展,网络安全已经成为人们无法回避的问题,因此为了保护现在越来越多的敏感信息,入侵检测技术也成为了一种非常重要的技术,得到了越来越多的重视。然而对其中一个重要部分―特征的自动选择的研究非常少。本文提出了一个EA用来执行特征的自动选择以及对RBF网络的自动优化。经过特征选择这个步骤可以显著的减少输入特征的数量,这样可以有效的减少过适应。此外,减少输入特征数目,还可以减少神经网络的执行时间。     

Scaling Up Inductive Logic Programming: An Evolutionary Wrapper Approach

Inductive logic programming (ILP) algorithms are classification algorithms that construct classifiers represented as logic programs. ILP algorithms have a number of attractive features, notably the ability to make use of declarative background (user-supplied) knowledge. However, ILP algorithms deal poorly with large data sets (>10⁴ examples) and their widespread use of the greedy set-covering algorithm renders them susceptible to local maxima in the space of logic programs.This paper presents a novel approach to address these problems based on combining the local search properties of an inductive logic programming algorithm with the global search properties of an evolutionary algorithm. The proposed algorithm may be viewed as an evolutionary wrapper around a population of ILP algorithms.The evolutionary wrapper approach is evaluated on two domains. The chess-endgame (KRK) problem is an artificial domain that is a widely used benchmark in inductive logic programming, and Part-of-Speech Tagging is a real-world problem from the field of Natural Language Processing. In the latter domain, data originates from excerpts of the Wall Street Journal. Results indicate that significant improvements in predictive accuracy can be achieved over a conventional ILP approach when data is plentiful and noisy.     

多机服务器任务调度的经济学方法q

本文针对多机服务器提出了一种任务调度的经济学方法以一般均衡理论为基础,依靠价格机制实现资源的优化分配;讨论了多机服务器的系统模型、任务的聚类和效用函数、经济学模型及均衡状态的最优性;最后通过模拟实验验证了经济学方法的有效性。     

Parallel Evolutionary Optimization of Multibody Systems with Application to Railway Dynamics

The optimization of multibody systems usually requires many costlycriteria computations since the equations of motion must be evaluated bynumerical time integration for each considered design. For activelycontrolled or flexible multibody systems additional difficulties ariseas the criteria may contain non-differentiable points or many localminima.Therefore, in this paper a stochastic evolution strategy is used incombination with parallel computing in order to reduce the computationtimes whilst keeping the inherent robustness. For the parallelization amaster-slave approach is used in a heterogeneous workstation/PCcluster. The pool-of-tasks concept is applied in order to deal withthe frequently changing workloads of different machines in the cluster.In order to analyze the performance of the parallel optimizationmethod, the suspension of an ICE passenger coach, modelled as an elasticmultibody system, is optimized simultaneously with regard to severalcriteria including vibration damping and a criterion related to safetyagainst derailment. The iterative and interactive nature of a typicaloptimization process for technical systems is emphasized.     

Mooring Systems – A Multibody Dynamic Approach

A method for simulating the motion behaviour of moored floatingoffshore structures in the time domain is presented. Theinteraction between the fluid and the floating structure isconsidered using linear potential theory. The hydrodynamic forcesacting on the mooring lines are computed using a modified Morisonequation. A complete three-dimensional model of the structure andthe mooring lines is generated using a multibody system approachincluding a subsystem technique. The model results in a largenumber of degrees of freedom. In order to illustrate a practicalapplication of this method, an analysis of a moored ponton in arandom sea is presented. Different configurations of the systemare examined in order to evaluate the motion behaviour and therestoring forces. Comparisons are made with the naturalfrequencies of the damped system.     

An Operational Approach to Requirements Specification for Embedded Systems

The approach to requirements specification for embedded systems described in this paper is called "operational" because a requirements specification is an executable model of the proposed system interacting with its environment. The approach is embodied by the language PAISLey, which is motivated and defined herein. Embedded systems are characterized by asynchronous parallelism, even at the requirements level; PAISLey specifications are constructed by interacting processes so that this can be represented directly. Embedded systems are also characterized by urgent performance requirements, and PAISLey offers a formal, but intuitive, treatment of performance.     

一种用于电子政务系统集成建模的方法

本文介绍了当前电子政务系统集成中面临的一些问题,并结合电子政务系统的特点,利用Web Services技术和MVC模式,提出一种新的电子政务系统集成模型,该模型能够很好地解决当前电子政务系统中原有工作流子系统异构集成的问题,并能提高系统集成后的可管理性。     

一种面向可信的工作流系统监控方法

摘要：提出了一种面向可信的工作流系统监控方法,该方法可对工作流引擎执行性能、工作流实例状态、以及负载情况等系统信息进行实时监测,并能对运行的工作流实例进行动态调整,有效的保证了工作流系统的可靠性、安全性和鲁棒性。该方法应用到了面向可信的动态工作流管理系统中,验证了该方法的有效性。     

An Evolutionary Approach of Multicriteria Manufacturing Cell Formation

Several approaches to designing cellular manufacturing systems exist, but they are generally based on the optimization of a single criterion, such as the minimization of the inter-cell movement or the total cost. Unfortunately, in practice, it is often necessary to take into account multiple criteria. We present an approach based on a niched pareto evolutionary algorithm which does not rely on weights or distances to a target solution. The suggested method allows several solutions to be provided, so that a choice is offered to the decision maker, according to his/her particular constraints. A new recombination operator is also presented. This approach is illustrated on a cell formation problem using two criteria: the cell workload and the traffic between cells.     

The TTA's Approach to Resilience after Transient Upsets

The Time-Triggered Architecture, as architecture for safety-critical real-time applications, incorporates fault-tolerance mechanisms to ensure correct system operation despite failures. The primary fault hypothesis of the TTA claims to tolerate either the arbitrary failure of any one of its nodes or the passively arbitrary failure of any one of its communication channels. To cover these failure modes, active redundancy techniques are used, which basically means that nodes and channels are physically replicated. The primary fault hypothesis, is, however, not strong enough for certain applications that have to tolerate transient upsets of multiple, possibly all, components in the system. Such a transient upset of the system may break up the synchrony of the nodes and leave disjoined sets of nodes synchronized to each other while the overall synchronization is lost. Although the TTA provides a clique avoidance algorithm that is able to correct a wide class of such multiple transient failures, a stronger algorithm is needed for full coverage. In this paper we discuss a secondary fault hypothesis for the TTA that addresses the transient upset of multiple components and present a new clique resolving algorithm based on the TTA's integrated diagnosis and startup service. This paper is a revised version of Steiner et al. (2003). This work has been funded by the European Project DECOS (Project number: IST-511764). Michael Paulitsch is currently affiliated with Honeywell International.