离散的SI和SIS传染病模型的研究

为了描述个体的死亡、染病者的恢复以及疾病的传染,引入了相应的概率.基于总种群中个体数量为常数的假设,根据染病者能否恢复分别建立了具有生命动力学的离散SI和SIS传染病模型.所得到的结果显示:它们具有与相应连续模型相同的动力学性态,并确定了各自的阈值.在它们的阈值之下,传染病最终将灭绝;在它们的阈值之上,传染病将会发展成为地方病,染病者的数量将趋向于一确定的正常数.     

高中数学专题研究的一个案例──关于沪闵路─春申路口红绿灯时间设置合理性的研究

一、专题的背景与分析 　　1. 背景 　　闵行区的沪闵路─春申路口是交通特别拥挤的交叉路口之一.家住莘庄地区的同学有一个共同的感受,在他们到校或回家路上必经的沪闵路─春申路口时常遇到塞车现象.……     

报童模型及ARMA预测在航空配餐问题中的应用

航班承载人数的不确定性,造成航空公司在配餐中利润的流失,现存的配餐模式存在较多的浪费.本文利用基于损失厌恶的报童模型和ARMA时间序列分析模型对深圳航空公司某航班的配餐份数进行了建模分析和预测,并通过对两种模型输出的比较,得出了长期预测与短期预测的模型应用理论.将实际的历史数据代人到模型中验证,其结果优于经验模式下的配餐盈利情况.本文所采用的研究方法和研究结果对航空公司的精益发展有建设性的意义.     

巧解一道"牛吃草"问题

"牛吃草"问题又称为消长问题,是17世纪英国伟大的科学家牛顿提出来的.典型牛吃草问题的条件是假设草的生长速度固定不变,不同头数的牛吃光同一片草地所需的天数各不相同,求若干头牛吃这片草地可以吃多少天.由于吃的天数不同,草又是天天在生长的,所以草的存量随吃的天数不断地变化.……     

大钟与六位密码

福尔摩西接到电话的时候正在翻看一本关于城中富翁艾伦王的传奇故事。艾伦王年少时靠贩卖廉价的小闹钟起家,经过几十年的辛勤努力,把自己的事业拓展成了最有名的钟表公司,是本市最有名的富翁。艾伦王年纪已经很大了,身体也不好。他的儿女众多,但是他们和艾伦王的关系并不融洽。大家都猜测他们对艾伦王的财富虎视眈眈。"喂,福尔摩西吗?"约翰焦急的声音从电话那头传过来。"是我。"福尔摩西回应道。     

Drucker-Prager准则可靠度解析

视岩体强度参数为正态分布随机变量,以可靠度理论为基础,推导了Drucker-Prager准则可靠度判别的解析表达式,并通过Monte-Carlo法和一次可靠度方法验证了其正确性.应用所得到的公式分析了岩体强度参数的变异性对屈服准则判别结果的影响.结果表明,强度参数的变异性对Drucker-Prager准则可靠概率的影响程度不尽相同,在变异系数较大的情况下,它们对可靠概率的影响显著,不可忽略.为岩体屈服的可靠度判别提供了一条新思路.     

导数在高考命题中的"五大"热点

导数作为大学的重要内容,进入中学数学教材后,给传统的内容注入了生机与活力,为中学数学命题的研究提供了新视角,新方法.由于导数是研究函数性质的一个很好的工具,它的用途十分广泛,它在解决函数、不等式、解析几何等问题有独到的功能.因此,近几年的高考正逐年加大对导数问题的考查力度,本文通过对07年全国各地高考题的整理和分析寻找命题规律,希望能对今后的教学提供一点复习思路.……     

也谈立体几何中的轨迹问题——从一道调考试题说起

2010-2011学年度武汉市部分学校新高三起点调研测试的一道试题引起了我的注意.原题如下: 在正四面体P-ABC中,M为△ABC内(含边界)一动点,且点M到三个侧面PAB,PBC,PCA的距离成等差数列,则点M的轨迹是 A.一条线段 B.椭圆的一部分 C.双曲线的一部分 D.抛物线的一部分     

股票收益率和新息的尾部估计

利用极值理论来考虑上证综指收益率的尾部.为了选择合理的超越门限,采用平均剩余函数和De-Haan矩估计相结合的方法.在学生t分布和广义误差分布的新患假设下,用GARCH和EGARCH新息的ARMA模型拟合指数收益率,并且使用极值理论的极大似然方法估计模型残差的尾指,估计结果表明收益率的尾指和模型的残差尾指基本一致.     

神奇的骰子

骰子在许多游戏中出现,特别是赌博游戏,当中蕴含了丰富的数学知识,引起了许多数学爱好者们的关注和研究.一个普通的骰子是正六面体,它的六个面分别为数字1,2,3,4,5,6,还有许多不同的骰子、不同的玩法,充满了神奇,但神奇的骰子背后离不开数学知识的支撑.本文将介绍三     

A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise

Evolution strategies are general, nature-inspired heuristics for search and optimization. Due to their use of populations of candidate solutions and their advanced adaptation schemes, there is a common belief that evolution strategies are especially useful for optimization in the presence of noise. Empirical evidence as well as a number of theoretical findings with respect to the performance of evolution strategies on a class of spherical objective functions disturbed by Gaussian noise support that belief. However, little is known with respect to the capabilities in the presence of noise of evolution strategies relative to those of other direct optimization strategies.In the present paper, theoretical results with respect to the performance of evolution strategies in the presence of Gaussian noise are summarized and discussed. Then, the performance of evolution strategies is compared empirically with that of several other direct optimizationstrategies in the noisy, spherical environment that the theoretical results have been obtained in. Due to the simplicity of that environment, the results are easily interpretable and can serve to reveal the respective strengths and weaknesses of the algorithms. It is seen that for low levels of noise, most of the strategies exhibit similar degrees of efficiency. For higher levels of noise, their step length adaptation scheme affords evolution strategies a greater degree of robustness than the other algorithms tested.     

Cellular rubber: Theoretical investigation on the basis of the mixture of theory and experiments

In this contribution we investigate a compressible cellular rubber experimentally and theoretically. In an appropriate model we apply the theory of porous media to describe the viscoelastic properties. The model parameters are identified by an algorithm based on evolution strategies. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Evolution of Fairness in the Ultimatum Game

The article presents a model on the evolution of strategies in the Ultimatum game. In particular, the model considers the effect of role turnover and interaction structure. Past experimental studies showed that subjects behave more fairly than would be predicted by classical game theory. The goal of this model is to illuminate how fairness emerges in evolution when role turnover and preferential association are in effect. Simulation shows that evolution towards strategies of fairness is plausible. Some empirical implications are also discussed.     

A simulation study for the distribution law of relative moments of evolution

Nine selection‐survival strategies were implemented in a genetic algorithm experiment, and differences in terms of evolution were assessed. The moments of evolution (expressed as generation numbers) were recorded in a contingency of three strategies (i.e., proportional, tournament, and deterministic) for two moments (i.e., selection for crossover and mutation and survival for replacement). The experiment was conducted for the first 20,000 generations in 46 independent runs. The relative moments of evolution (where evolution was defined as a significant increase in the determination coefficient relative to the previous generation) when any selection‐survival strategy was used fit a Log‐Pearson type III distribution. Moreover, when distributions were compared to one another, functional relationships were identified between the population parameters, revealing a degeneration of the Log‐Pearson type III distribution in a one‐parametrical distribution that can be assigned to the chosen variable—evolution strategy. The obtained theoretical population distribution allowed comparison of the selection‐survival strategies that were used. © 2012 Wiley Periodicals, Inc. Complexity, 2012.     

A note on clemhout and Wan's dynamic games of common property resources

Recently, Clemhout and Wan (Ref. 1) provided solutions to a class of stochastic differential game models concerning common property resources. However, they only established closed-loop strategies for the infinite-horizon problem using the Kushner test. In this paper, we provide closed-loop feedback solutions to their game in a finite-time horizon with deterministic evolution dynamics.The helpful comments of an anonymous referee are gratefully acknowledged.     

Dynamic multi-criteria evaluation of co-evolution strategies for solving stock trading problems

Risk and return are interdependent in a stock portfolio. To achieve the anticipated return, comparative risk should be considered simultaneously. However, complex investment environments and dynamic change in decision making criteria complicate forecasts of risk and return for various investment objects. Additionally, investors often fail to maximize their profits because of improper capital allocation. Although stock investment involves multi-criteria decision making (MCDM), traditional MCDM theory has two shortfalls: first, it is inappropriate for decisions that evolve with a changing environment; second, weight assignments for various criteria are often oversimplified and inconsistent with actual human thinking processes.In 1965, Rechenberg proposed evolution strategies for solving optimization problems involving real number parameters and addressed several flaws in traditional algorithms, such as their use of point search only and their high probability of falling into optimal solution area. In 1992, Hillis introduced the co-evolutionary concept that the evolution of living creatures is interactive with their environments (multi-criteria) and constantly improves the survivability of their genes, which then expedites evolutionary computation. Therefore, this research aimed to solve multi-criteria decision making problems of stock trading investment by integrating evolutionary strategies into the co-evolutionary criteria evaluation model. Since co-evolution strategies are self-calibrating, criteria evaluation can be based on changes in time and environment. Such changes not only correspond with human decision making patterns (i.e., evaluation of dynamic changes in criteria), but also address the weaknesses of multi-criteria decision making (i.e., simplified assignment of weights for various criteria).Co-evolutionary evolution strategies can identify the optimal capital portfolio and can help investors maximize their returns by optimizing the preoperational allocation of limited capital. This experimental study compared general evolution strategies with artificial neural forecast model, and found that co-evolutionary evolution strategies outperform general evolution strategies and substantially outperform artificial neural forecast models. The co-evolutionary criteria evaluation model avoids the problem of oversimplified adaptive functions adopted by general algorithms and the problem of favoring weights but failing to adaptively adjust to environmental change, which is a major limitation of traditional multi-criteria decision making. Doing so allows adaptation of various criteria in response to changes in various capital allocation chromosomes. Capital allocation chromosomes in the proposed model also adapt to various criteria and evolve in ways that resemble thinking patterns.     

On the stability of evolutionary dynamics in games with incomplete information

In an interaction it is possible that one agent has features it is aware of but the opponent is not. These features (e.g. cost, valuation or fighting ability) are referred to as the agent’s type. The paper compares two models of evolution in symmetric situations of this kind. In one model the type of an agent is fixed and evolution works on strategies of types. In the other model every agent adopts with fixed probabilities both types, and type-contingent strategies are exposed to evolution. It is shown that the dynamic stability properties of equilibria may differ even when there are only two types and two strategies. However, in this case the dynamic stability properties are generically the same when the payoff of a player does not depend directly on the type of the opponent. Examples illustrating these results are provided.     

Geometric surface evolution with tangential contribution

Surface processing tools based on Partial Differential Equations (PDEs) are useful in a variety of applications in computer graphics, digital animation, computer aided modelling, and computer vision. In this work, we deal with computational issues arising from the discretization of geometric PDE models for the evolution of surfaces, considering both normal and tangential velocities. The evolution of the surface is formulated in a Lagrangian framework. We propose several strategies for tangential velocities, yielding uniform redistribution of mesh points along the evolving family of surfaces, preventing computational instabilities and increasing the mesh regularity. Numerical schemes based on finite co-volume approximation in space will be considered. Finally, we describe how this framework may be employed in applications such as mesh regularization, morphing, and features preserving surface smoothing.     

On the stability of evolutionary dynamics in games with incomplete information

In an interaction it is possible that one agent has features it is aware of but the opponent is not. These features (e.g. cost, valuation or fighting ability) are referred to as the agent’s type. The paper compares two models of evolution in symmetric situations of this kind. In one model the type of an agent is fixed and evolution works on strategies of types. In the other model every agent adopts with fixed probabilities both types, and type-contingent strategies are exposed to evolution. It is shown that the dynamic stability properties of equilibria may differ even when there are only two types and two strategies. However, in this case the dynamic stability properties are generically the same when the payoff of a player does not depend directly on the type of the opponent. Examples illustrating these results are provided.     

Life‐like self‐reproducers

In order to understand the interplay among information, genetic instructions, and phenotypic variations, self‐reproducers discovered in two‐dimensional cellular automata are considered as proto‐organisms, which undergo to mutations as they were in a real environmental situation. We realized a computational model through which we have been able to discover the genetic map of the self‐reproducers and the networks they use. Identifying in these maps sets of different functional genes, we found that mutations in the genetic sequences could affect both external shapes and behavior of the self‐reproducers, thus realizing different life‐like strategies in the evolution process. The results highlight that some strategies evolution uses in selecting organisms that are fitting with changing environmental situations maintain the self‐reproducing function, whereas other variations create new self‐reproducers. These self‐reproducers in turn realize different genetic networks, which can be very different from the basic ancestors pools. The mutations that are disruptive bring self‐reproducers to disappear, while other proto‐organisms are generated. © 2004 Wiley Periodicals, Inc. Complexity 9: 38–55, 2004