基于中性进化、自组织和自然选择的进化算法

生物的进化同时在基因层、个体层和种群层进行。基因层上的进化是随机、均匀、无方向性的；个体层的随机行为通过自组织作用形成种群的复杂行为；种群的进化则是一个以环境为参考的自然选择过程。基于此，本文提出了一种基于中性进化，自组织和自然选择的进化算法．该算法同时考虑基因层、个体层和种群层上的进化过程以及三个层次间的相互作用和映射关系。提出了个体能力评估函数f(xi)的概念，分析了个体能力评估函数f(xi)与种群适应度函数fit(X)间的关系。时该算法的性能进行了仿真研究．仿真结果表明该算法相对于传统的进化算法具有更好的全局收敛性，更快的收敛速度和更强的参数鲁棒性。     

基于自组织和自然选择的进化算法

生物的进化同时在基因层，个体层和种群层进行。基因层上的进化是随机、均匀、无方向性的，个体层的随机行为通过自组织作用形成种群的复杂行为，种群的进化则是一个以环境为参考的自然选择过程，基于这样的进化观点，提出了一种基于自组织和自然选择的进化算法，该算法同时考虑基因层、个体层和种群层上的进化过程。对该算法的性能进行了仿真研究，仿真结果表明该算法具有更好的全局收敛性，更快的收敛速度和更强的参数鲁棒性。     

多Agent系统中的协同进化研究

协同进化作为多Agent系统研究的一个重要手段，近几年得到了广泛的关注和快速发展，渐渐成为进化研究的一个主流方向。该文系统介绍了协同进化的研究现状，分析总结了协同进化的基本特点，并指出了协同进化可能的发展方向。     

基于自组织演化的多Agent可信联盟研究

不同于现有采用博弈论的方法研究多Agent联盟技术,从联盟信任这一角度开展可信联盟研究,首先在历史合作信息的基础上构建个体信任度,在此基础上建立联盟信任度,进而形成可信联盟.通过联盟间的竞价协商来使得多个小规模的联盟合并成规模更大的联盟从而完成复杂的任务,刻画出联盟演化的自组织性并将信任贯穿于联盟的演化过程.为获得稳定的联盟,在联盟内部收益分配的机制上采用自由竞价与信任评估方式,最后在联盟收益分配结束后,通过信任修正来解决自由竞价中的欺诈行为,并同时获得数据隐私保护,使得联盟内部成员获得公平合理而又高效的分配机制.通过联盟信任,建立起分布式合作模型,可以大规模地降低计算复杂度和可控的风险收益,为动态联盟的稳定性提供有效保障.     

Agent组织研究综述

Agent组织为开放环境下大规模协调和协作问题提供了一种解决方案。近年来对Agent组织的研究越来越引起重视,并已成为多Agent领域的一个重要分支。文章介绍了Agent组织产生的背景、Agent组织的定义,并从Agent组织形成的角度对Agent组织进行分类;综述了近年来提出的面向组织构建多Agent系统的方法并对它们进行了比较;分析了Agent组织的演化,并指出了今后的方向。     

基于Agent图的三级多Agent系统组织模型

多Agent系统的组织结构是Agent个体之间交互的框架。对分布式多Agent系统的组织方式、协作机制进行了简要讨论,提出了Agent域及Agent图的概念。根据不同Agent之间的地理位置和通信代价,由Agent个体、Agent组及Agent域三级组织结构形成一个Agent图,并借鉴计算机网络的分布式自适应路由选择策略进行多Agent系统的协作组织。分析表明,该模型具有高效、健壮、通信开销较小等优点。     

基于多Agent和进化算法的作业线调度研究

作业线调度是集装箱码头生产调度中最为关键的一个环节。文中将在充分分析与研究集装箱码头装卸作业信息流动的基础上,针对岸边作业线调度问题以及国内作业线系统的不足,建立模型辅助决策,帮助调度员快速、准确、安全、合理地进行岸桥调度的决策。在文中,充分地考虑了泊位、船舶、岸桥等集装箱设备及其协调,利用并合理地配置了这些码头资源,将引用多Agent技术协调作业线调度,并使用进化算法计算并安排岸桥来进行作业调度,建立基于多Agent和进化算法的集装箱码头作业线调度智能化系统。     

Agent组织的一种递归模型

Agent组织是多Agent系统(MAS)的一种求解形式.基于Agent组织的问题求解可以减少MAS中Agent之间交互的复杂性,降低求解难度.结合收益和组织规则提出了一种Agent组织的递归模型,并讨论了Agent组织的目标分解、收益计算和组织规则形成等问题.相对于Ferber和Jennings等人的工作,这种模型适于描述不同规模的组织,有利于MAS宏观分析和微观分析的结合,而且模型中效用参量的引入可以在一定程度上表明Agent组织的演化.     

基于进化博弈的网格资源分配方法的研究

在网络资源优化分配问胚的研究中,由于用户存在决策失误,现有基于理性用户博弈的网格资源分配在实际网格环境会完全失效.分析了非完全理性网格用户群体的资源分配策略及其演化过程,提出一种改进的复制动态机制的网格资源分配方法,克服了用户理性的限制,引入变异机制,在无初始学习样本的情况下,也能确保用户达到进行稳定策略点,实现了网格资源在有限理性用户之间的优化分配.仿真表明,用户通过学习对资源分配策略进行调整,可实现最优分配策略并处于稳定状态,证明了进化博弈的资源分配方法在网格环境中的适应性和稳定性.     

基于π演算的一种Agent组织模型

Agent组织是多Agent系统研究的重要方面。从直观含义出发，将组织看做Agent之间的控制关系，以π演算为工具，给出了组织角色的定义和根据组织框架构建具体组织的过程。角色和组织框架都用进程表示，确定了Agent加入和脱离组织的严格描述，相比Ferber等以π演算和化学自动机定义组织形成过程的形式语义的工作，本文显式定义了角色和组织框架，所讨论的组织更具一般性。     

Evolutionary Dynamics in Public Good Games

This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The dynamic game is described by an Evolutionary Algorithm, which is shown to extend the applicability of ordinary replicator dynamics of evolutionary game theory to problem sets characterized by finite populations and continuous strategy spaces. We analyze the learning process of purely and impurely altruistic agents and find in both cases the contribution level to converge towards the Nash equilibrium. The group size, the degree of initial heterogeneity and the propensity to experiment are key factors of the learning process. JEL Classifications: C6, C73, D83, H41     

Fast Evolutionary Learning with Batch-Type Self-Organizing Maps

Although no distance function over the input data is definable, it is still possible to implement the self-organizing map (SOM) process using evolutionary-learning operations. The process can be made to converge more rapidly when the probabilistic trials of conventional evolutionary learning are replaced by averaging using the so-called Batch Map version of the self-organizing map. Although no other condition or metric than a fitness function between the input samples and the models is assumed, an order in the map that complies with the functional similarity of the models can be seen to emerge. There exist two modes of use of this new principle: representation of nonmetric input data distributions by models that may have variable structures, and fast generation of evolutionary cycles that resemble those defined by the genetic algorithms. The spatial order in the array of models can be utilized for finding more uniform variations, such as crossings between functionally similar models.     

The Nash Equilibrium Point of Dynamic Games Using Evolutionary Algorithms in Linear Dynamics and Quadratic System

In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can’t be claimed that these techniques of solution always obtain the optimal and actual Nash equilibrium point. For solving these problems, two evolutionary algorithms are then presented based on the population to solve general dynamic games. The first algorithm is based on the genetic algorithm and we use genetic algorithms to model the players' learning process in several models and evaluate them in terms of their convergence to the Nash Equilibrium. in the second algorithm, a Particle Swarm Intelligence Optimization (PSO) technique is presented to accelerate solutions’ convergence. It is claimed that both techniques can find the actual Nash equilibrium point of the game keeping the problem’s generality and without imposing any limitation on it and without being caught by the local Nash equilibrium point. The results clearly show the benefits of the proposed approach in terms of both the quality of solutions and efficiency.     

Evolving Compositionality in Evolutionary Language Games

Evolutionary language games have proved a useful tool to study the evolution of communication codes in communities of agents that interact among themselves by transmitting and interpreting a fixed repertoire of signals. Most studies have focused on the emergence of Saussurean codes (i.e., codes characterized by an arbitrary one-to-one correspondence between meanings and signals). In this contribution, we argue that the standard evolutionary language game framework cannot explain the emergence of compositional codes-communication codes that preserve neighborhood relationships by mapping similar signals into similar meanings-even though use of those codes would result in a much higher payoff in the case that signals are noisy. We introduce an alternative evolutionary setting in which the meanings are assimilated sequentially and show that the gradual building of the meaning-signal mapping leads to the emergence of mappings with the desired compositional property.     

Evolutionary Hough Games for coherent object detection

In this paper we propose a novel, game-theoretic approach for finding multiple instances of an object category as sets of mutually coherent votes in a generalized Hough space. Existing Hough-voting based detection systems have to inherently apply parameter-sensitive non-maxima suppression (NMS) or mode detection techniques for finding object center hypotheses. Moreover, the voting origins contributing to a particular maximum are lost and hence mostly bounding boxes are drawn to indicate the object hypotheses. To overcome these problems, we introduce a two-stage method, applicable on top of any Hough-voting based detection framework. First, we define a Hough environment, where the geometric compatibilities of the voting elements are captured in a pairwise fashion. Then we analyze this environment within a game-theoretic setting, where we model the competition between voting elements as a Darwinian process, driven by their mutual geometric compatibilities. In order to find multiple and possibly overlapping objects, we introduce a new enumeration method inspired by tabu search. As a result, we obtain locations and voting element compositions of each object instance while bypassing the task of NMS. We demonstrate the broad applicability of our method on challenging datasets like the extended TUD pedestrian crossing scene.     

Replicator Dynamics of Evolutionary Hypergames

In evolutionary game theory, the distribution of strategies in the population is changed according to payoffs which individuals earn depending on their selected strategies. However, to the best of our knowledge, individuals' perceptions in evolutionary games have been disregarded. Individuals that select a strategy by trial and error may perceive the same conflict situation from different viewpoints. Hence, the influence of individuals' perceptions has to be taken into consideration. In this correspondence, interpretation functions in hypergames are introduced into evolutionary games. Using them, we formulate replicator dynamics considering individuals' perceptions. In a special case that there exist two populations, we show that its interior equilibrium point is stable if the corresponding point is a hyper-Nash-Pareto pair     

A Kohonen Self-Organizing Map Approach to Modeling Growth Pole Dynamics

Economic growth poles remain an important concept in regional development policy analysis. Recent attention has focused for example on the emergence and promotion of technopoles, or agglomerations of high technology activities within a region. This paper presents a neural network approach for analyzing these evolutionary processes. Kohonen self-organizing maps are used to simulate the spread and backwash effects associated with the emergence of regional growth poles. Unlike other models of growth pole development, which rely heavily on conventional economic theory, the approach outlined in this paper allows for increasing returns, multiple equilibria, `satisficing' behavior, imperfect information and stochasticity. The model could be used to explore spatio-temporal dynamics under different assumptions relating to industrial composition, interindustry linkages, and the spatial juxtaposition of firms (industries). The model is applied to the Washington, D.C. metropolitan area, and the patterns of development that have arisen from the location behavior of high technology firms in this region.     

Evolutionary Dynamics on Scale-Free Interaction Networks

There has been a recent surge of interest in studying dynamical processes, including evolutionary optimization, on scale-free topologies. While various scaling parameters and assortativities have been observed in natural scale-free interaction networks, most previous studies of dynamics on scale-free graphs have employed a graph-generating algorithm that yields a topology with an uncorrelated degree distribution and a fixed scaling parameter. In this paper, we advance the understanding of selective pressure in scale-free networks by systematically investigating takeover times under local uniform selection in scale-free topologies with varying scaling exponents, assortativities, average degrees, and numbers of vertices. We demonstrate why the so-called characteristic path length of a graph is a nonlinear function of both scaling and assortativity. Neither the eigenvalues of the adjacency matrix nor the effective population size was sufficient to account for the variance in takeover times over the parameter space that was explored. Rather, we show that 97% of the variance of logarithmically transformed average takeover times, on all scale-free graphs tested, could be accounted for by a planar function of: 1) the average inverse degree (which captures the effects of scaling) and 2) the logarithm of the population size. Additionally, we show that at low scaling exponents, increasingly positive assortativities increased the variability between experiments on different random graph instances, while increasingly negative assortativities increased the variability between takeover times from different initial conditions on the same graph instances. We explore the mechanisms behind our sometimes counterintuitive findings, and discuss potential implications for evolutionary computation and other relevant disciplines.      

猜数游戏与编码理论

本文揭示了一类猜数游戏与编码理论中的奇偶校验码以及海明码之间的联系,并利用奇偶校验码和海明码,提出了这类游戏的解决方案。