On the size of computationally complete hybrid networks of evolutionary processors

A hybrid network of evolutionary processors (an HNEP) is a graph where each node is associated with an evolutionary processor (a special rewriting system), a set of words, an input filter and an output filter. Every evolutionary processor is given with a finite set of one type of point mutations (an insertion, a deletion or a substitution of a symbol) which can be applied to certain positions of a string over the domain of the set of these rewriting rules. The HNEP functions by rewriting the words that can be found at the nodes and then re-distributing the resulting strings according to a communication protocol based on a filtering mechanism. The filters are defined by certain variants of random-context conditions. HNEPs can be considered as both language generating devices (GHNEPs) and language accepting devices (AHNEPs). In this paper, by improving the previous results, we prove that any recursively enumerable language can be determined by a GHNEP and an AHNEP with 7 nodes. We also show that the families of GHNEPs and AHNEPs with 2 nodes are not computationally complete.     

Breaking the data encryption standard using networks of evolutionary processors with parallel string rewriting rules

In this paper we introduce a biologically inspired distributed computing model called networks of evolutionary processors with parallel string rewriting rules (NEPPS), which is a variation of the hybrid networks of evolutionary processors introduced by Martin-Vide et al. Such a network contains simple processors that are located in the nodes of a virtual graph. Each processor has strings (each string having multiple copies) and string rewriting rules. The rules are applied parallely on the strings. After the strings have been rewritten, they are communicated among the processors through filters. We show that we can theoretically break the DES (data encryption standard), which is the most widely used cryptosystem, using NEPPS. We prove that, given an arbitrary <plain-text, cipher-text> pair, one can recover the DES key in a constant number of steps.     

Performance improvement of self-adaptive evolutionary methods with a dynamic lower bound

Recent research on self-adaptive evolutionary programming (EP) methods evidenced the problem of premature convergence. Self-adaptive evolutionary programming methods converge prematurely because their object variables evolve more slowly than do their strategy parameters, which subsequently leads to a stagnation of object variables at a non-optimum value. To address this problem, a dynamic lower bound has been proposed, which is defined here as the differential step lower bound (DSLB) on the strategy parameters. The DSLB on an object variable depends on its absolute distance from the corresponding object variable of the best individual in the population pool. The performance of the self-adaptive EP algorithm with DSLB has been verified over eight different test functions of varied complexities.     

EHPI主机接口在多处理器系统中的应用

介绍了TMS320C5510 DSP的16位增强型主机接口EHPI在多处理系统中的应用，具体分析了多处理系统中利用EHPI进行通信以及上电复位软件加载的方案，详细介绍了EHPI的特点及实现方法。     

Incorporating characteristics of human creativity into an evolutionary art algorithm

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.

Multi-objective evolutionary approaches for intelligent design of sensor networks in the petrochemical industry

The design of optimal sensor networks for an industrial process is a complex problem that requires the resolution of several tasks with a high level of expertise. The first of these subproblems consists in selecting an initial sensor network as the starting point for the instrumentation design. This particular task constitutes a combinatorial optimization problem, where several goals are prosecuted by the designer. Therefore, the initialization procedure can be defined as a multi-objective optimization problem. In this paper, the use of multi-objective evolutionary approaches to assist experts in the design of an initial sensor network is proposed and analyzed. The aim is to contrast the advantages and limitations of Pareto and non-Pareto techniques in the context of this industrial application. The algorithms consider objectives related to cost, reliability and level of information associated with a sensor network. The techniques were evaluated by means of a comparative analysis for a strongly non-linear mathematical model that represents an ammonia synthesis plant. Results have been contrasted in terms of the set coverage and spacing metrics. As a final conclusion, the non-Pareto strategy converged closer to the Pareto front than the Pareto-based algorithms. In contrast, the Pareto-based algorithms achieved better relative distance among solutions than the non-Pareto method. In all cases, the use of evolutionary computation is useful for the expert to take the final decision on the preferred initial sensor network.     

Analysing stability and investments in railway networks using advanced evolutionary algorithms

We consider a network of periodically running railway lines. Investments are possible to increase the speed and to improve the synchronisation of trains. The model also includes random delays of trains and the propagation of delays across the network. We derive a cost‐benefit analysis of investments, where the benefit is measured in reduced waiting time for passengers changing lines. We also estimate the actual mean waiting time simulating the train delays. This allows us to analyse the impact that an increasing synchronisation of the timetable has on its stability. Simulation is based on an analytical model obtained from queueing theory. We use sophisticated adaptive evolutionary algorithms, which send off avant‐garde solutions from time to time to speed up the optimisation process. As there is a high correlation between scheduled and estimated waiting times for badly synchronised timetables, we are even able to include the time consuming simulation into our optimisation runs.     

The impact of parametrization in memetic evolutionary algorithms

Memetic (evolutionary) algorithms integrate local search into the search process of evolutionary algorithms. As computational resources have to be spread adequately among local and evolutionary search, one has to care about when to apply local search and how much computational effort to devote to local search. Often local search is called with a fixed frequency and run for a fixed number of iterations, the local search depth. There is empirical evidence that these parameters have a significant impact on performance, but a theoretical understanding as well as concrete design guidelines are missing.     

A stochastic evolutionary coalition game model of secure and dependable virtual service in Sensor-Cloud

Various intrusion detection systems (IDSs) have been proposed in recent years to provide safe and reliable services in cloud computing. However, few of them have considered the existence of service attackers who can adapt their attacking strategies to the topology-varying environment and service providers’ strategies. In this paper, we investigate the security and dependability mechanism when service providers are facing service attacks of software and hardware, and propose a stochastic evolutionary coalition game (SECG) framework for secure and reliable defenses in virtual sensor services. At each stage of the game, service providers observe the resource availability, the quality of service (QoS), and the attackers’ strategies from cloud monitoring systems (CMSs) and IDSs. According to these observations, they will decide how evolutionary coalitions should be dynamically formed for reliable virtual-sensor-service composites to deliver data and how to adaptively defend in the face of uncertain attack strategies. Using the evolutionary coalition game, virtual-sensor-service nodes can form a reliable service composite by a reliability update function. With the Markov chain constructed, virtual-sensor-service nodes can gradually learn the optimal strategy and evolutionary coalition structure through the minimax-Q learning, which maximizes the expected sum of discounted payoffs defined as QoS for virtual-sensor-service composites. The proposed SECG strategy in the virtual-sensor-service attack-defense game is shown to achieve much better performance than strategies obtained from the evolutionary coalition game or stochastic game, which only maximizes each stage's payoff and optimizes a defense strategy of stochastic evolutionary, since it successfully accommodates the environment dynamics and the strategic behavior of the service attackers.     

The evolutionary cost of Baldwin effect in the routing and spectrum allocation problem in elastic optical networks

Evolutionary Algorithms (EAs) are frequently used to solve various practical problems. It is common to adjust an EA to the solved problem by adding a different kind of problem-dependent mechanisms. One of the possible improvements is an evolutionary method hybridization with local search algorithms. Such hybridization may lead to phenomena called the Baldwin effect. It was shown that the occurrence of Baldwin effect helps to preserve population diversity and therefore may be beneficial for the method effectiveness. However, it also has its drawbacks. The use of local search causes the significant increase of computation load necessary for a single individual’s fitness computation. Therefore, the hybridization of an evolutionary method does not have to be beneficial. Moreover, the benefits (or drawbacks) of hybridization may be different depending on the method type and the features of the solved problem. Therefore, the main objective of this paper is to investigate the pros and cons of hybridization on the base of a hard practical and up-to-date problem, namely the Routing and Spectrum Allocation of Multicast Flows (RSA/M) in Elastic Optical Networks (EONs). The second objective of this paper is to propose an effective optimization method for solving the RSA/M problem in EONs.     

Artificial Neural Network support to monitoring of the evolutionary driven security aware scheduling in computational distributed environments

Monitoring of the system performance in highly distributed computing environments is a wide research area. In cloud and grid computing, it is usually restricted to the utilization and reliability of the resources. However, in today’s Computational Grids (CGs) and Clouds (CCs), the end users may define the special personal requirements and preferences in the resource and service selection, service functionality and data access. Such requirements may refer to the special individual security conditions for the protection of the data and application codes. Therefore, solving the scheduling problems in modern distributed environments remains still challenging for most of the well known schedulers, and the general functionality of the monitoring systems must be improved to make them efficient as schedulers supporting modules.In this paper, we define a novel model of security-driven grid schedulers supported by an Artificial Neural Network (ANN). ANN module monitors the schedule executions and learns about secure task–machine mappings from the observed machine failures. Then, the metaheuristic grid schedulers (in our case—genetic-based schedulers) are supported by the ANN module through the integration of the sub-optimal schedules generated by the neural network, with the genetic populations of the schedules.The influence of the ANN support on the general schedulers’ performance is examined in the experiments conducted for four types of the grid networks (small, medium, large and very large grids), two security scheduling modes—risky and secure scenarios, and six genetic-based grid schedulers. The generated empirical results show the high effectiveness of such monitoring support in reducing the values of the major scheduling criteria (makespan and flowtime), the run times of the schedulers and the grid resource failures.     

应用于无线传感器网络的DC-DC升压转换器设计

无线传感器网络的应用越来越广泛,根据从环境中获取能量的需要,提出一种两级电荷泵结构的DC-DC升压转换器,通过两级升压将热电发电机产生的低压升至工作电压,该结构具有体积小、转换效率高等优点。仿真结果表明:使用0.18μm CMOS低阈值电压工艺时,能够将低压0.1 V升至2.5 V,在输入电压为0.1 V时,转换效率为45%,功耗为3.1μW。     

神经网络在密码学领域的应用

神经网络是近年来得到迅速发展的新型学科,由于其独特的结构和处理信息的方法,使其应用范围日益扩大,并已在许多学科领域取得了成功的应用,其中通信保密通信就是神经网络的重要研究领域,密码技术作为保密通信的关键已成为当代信息高科技的热点,用人工神经网络解决计算机密码学中的问题,即神经密码学,不仅是神经网络的一个重要应用研究领域,而且也将给密码学理论提供一种全新的思路。     

网络技术在冀东数字化油田建设中的应用

落后的网络通讯技术是制约油田地面工程数字化建设和升级改造的技术瓶颈。在油田向信息化、数字化迈进的过程中,如何解决监控数据、视频信息稳定高速传输是大多数油田都要面临的问题。结合冀东数字化油田建设通讯网络的具体需求,本文详细描述了冀东油田作业区在通讯组网的具体技术方案及升级改造后的使用效果。     

Self-emergence of a common lexicon by evolution in teams of autonomous agents

In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of evolutionary strategies to the self-emergence of a common lexicon in a population of agents. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the agents or the states of the environment itself) into symbols or signals we check whether it is possible for the population to converge in an autonomous, decentralized way to a common lexicon, so that the communication efficiency of the entire population is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with evolutionary strategies to an optimal Saussurean communication system. We have organized our experiments alongside two main lines: first, we have investigated the effect of the population size on the convergence results. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the population of agents we have defined the population's consensus when all the agents (i.e. 100% of the population) share the same association matrix or lexicon. As a general conclusion we have shown that evolutionary strategies are powerful enough optimizers to guarantee the convergence to lexicon consensus in a population of autonomous agents.     

工业以太网中网络通信技术的研究

随着以太网技术的迅猛发展，工业以太网在工业控制领域扮演着越来越重要的角色。该文在阐述工业以太网通信协议模型基础上．针对当前工业控制领域提出的将测控分散到现场、实现远程监控的要求，提出了一种实现工业以太网数据通信技术的可行的方案。按该方案设计的系统已得到实现．应用结果证明此种通信方案高效可靠。能够满足工业以太网对于网络通信的要求。     

The role of modeling in computational science education

Modeling and simulation skills are two core competences of computational science and thus should be a central part of any curriculum. While there is a well-founded methodology for the design of simulation algorithms today the teaching of modeling skills carries some intrinsic problems. The reason is that modeling is still partly an art and partly a science. As an important consequence for university education, the concepts for teaching modeling must be quite different from those for teaching simulation algorithms. Experiences made with the courses on ‘Modeling and Simulation’ at the University of Siegen are summarized and some general concepts for the teaching of modeling skills are presented. In particular, three practical approaches to modeling education are discussed with several examples.     

Investigating the role of metallic fillers in particulate reinforced flexible mould material composites using evolutionary algorithms

To reduce the cooling time in soft tooling (ST) process, high thermal conductive fillers (such as metallic filler) are included in flexible mould material. But addition of metallic fillers affects various properties of ST process and the influences may vary according to the types of materials used. Therefore, in order to investigate the role of various metallic fillers in particulate reinforced flexible mould material composites, multi-objective optimizations of maximizing equivalent thermal conductivity and minimizing effective modulus of elasticity of composite mould materials are conducted using evolutionary algorithms (EAs). Here we have adopted two EA-based algorithms namely NSGAII and SPEA2 in order to solve the present problem independently. Comparative study of the results reveals that NSGAII performs better over SPEA2 for investigating the role of metallic fillers in particulate reinforced flexible mould material composites. A recently proposed innovization procedure is also used to unveil salient properties associated with the obtained trade-off solutions. These solutions are analyzed to study the role of various parameters influencing the equivalent thermal conductivity and modulus of elasticity of the composite mould material. Based on the findings through investigations, the optimal selection of materials is suggested including the cost implication factor.     

PLC在污水处理厂控制系统中的应用

主要介绍了污水处理厂控制系统的组成以及各部分的功能,重点介绍了采用溶解氧仪和氧化还原电位计结合起来的曝气控制模型。