基于遗传算法的二层线性规划问题的求解算法

本研究了下层以最优解返回上层的二层线性规划问题的遗传算法。在提出可行度概念的基础上，构造了二层线性规划上层规划问题的适应度函数，由此设计了求解二层线性规划问题遗传算法。为了提高遗传算法处理约束的能力，在产生初始种群时将随机产生的初始种群变为满足约束的初始种群，从而避免了使用罚函数处理约束带来的困难，最后用实例验证了本提出的二层线性规划的遗传算法的有效性。     

Local Rules and Fitness Landscapes: A Catastrophe Model

This paper examines the imposition of local rules in five mail delivery centres of Australia Post. Local rules are patterns of behaviour used by subunits of an organization to optimise their payoff. These local rules may ultimately benefit the total organization. Fitness landscapes are used to examine the emergence of local rules in this workplace. This research examined the relationship between the time taken to sort the mail and the volume of this mail. Work rates show a clear catastrophe shift; that is, work rates suddenly drop when the volume of mail exceeds a certain level, as Postal Delivery Officers apply local rules to maximise gains inherent in the pay structures. Such behaviour is close to that predicted by Kauffman (1995) in computer simulations of lattices and may be indicative of the application of local rules in organizations. The implications of the use of local rules are that behaviour in social systems may be dictated by systemic and emergent processes which are outside immediate management control. A further implication is that organizations may be structured to a significant extent by such local rules.     

ELIHKSIR Web Server: Evolutionary Links Inferred for Histidine Kinase Sensors Interacting with Response Regulators

Two-component systems (TCS) are signaling machinery that consist of a histidine kinases (HK) and response regulator (RR). When an environmental change is detected, the HK phosphorylates its cognate response regulator (RR). While cognate interactions were considered orthogonal, experimental evidence shows the prevalence of crosstalk interactions between non-cognate HK–RR pairs. Currently, crosstalk interactions have been demonstrated for TCS proteins in a limited number of organisms. By providing specificity predictions across entire TCS networks for a large variety of organisms, the ELIHKSIR web server assists users in identifying interactions for TCS proteins and their mutants. To generate specificity scores, a global probabilistic model was used to identify interfacial couplings and local fields from sequence information. These couplings and local fields were then used to construct Hamiltonian scores for positions with encoded specificity, resulting in the specificity score. These methods were applied to 6676 organisms available on the ELIHKSIR web server. Due to the ability to mutate proteins and display the resulting network changes, there are nearly endless combinations of TCS networks to analyze using ELIHKSIR. The functionality of ELIHKSIR allows users to perform a variety of TCS network analyses and visualizations to support TCS research efforts.     

基于相对误差意义下的最小二乘法

最小二乘法一直广泛应用于科学技术各领域 ,但仅适用于等精度数据———因其评价依据是大致相同的绝对误差。而大多数科研及观测数据往往按被观测量的相对误差进行评价 ,即被观测量愈大 ,允许及实际测量误差也愈大。从这个意义上讲 ,若以相对拟合误差的平方和最小为条件 ,建立一套新的最小二乘法 ,那么当然可以期望新方法将具更广泛的用途和实际的意义。本文给出了新最小二乘法的实用结果     

二层线性规划的自适应遗传算法

提出了一种自适应遗传算法来求解二层线性规划问题.该方法克服了难以确定合适的交叉概率和变异概率的困难.另外,在该方法中还采用了其它一些技巧不仅解决了在采用遗传算法经常出现的有些个体不可行的问题,而且还改进了算法的效率.     

一个水流问题的遗传算法求解

应用遗传算法(GA)来讨论一个水流问题.这个水流问题曾是不少统计学者用来考察不同试验设计和建模方法的常用案例.通过本例旨在说明遗传算法确为求解复杂系统优化问题的有力工具.     

Local Rules: Emergence on Organizational Landscapes

This paper proposes that the theory of local rules provides a model for explaining organizational behavior as an emergent property of a fitness landscape. While local rule theory has its genesis in evolutionary biology, this paper links it to work in computational mathematical organizational theory. It further proposes that there are conditions, characterized by coadaptation, under which rules will survive in relatively stable forms, and other conditions, characterized by competition, under which local rules will change. The paper then discusses how catastrophe analysis can provide insights into changing patterns of organizational interactions. A discussion of methodology outline shows developments in agent-based simulation modeling can contribute to the development of local rule theory.     

Complete Condensation of Zero Range Process in Fitness Networks

In current paper we study the so-called “complete condensation” of zero range process on the fitness network. It is found that under the high temperature limit, the condensation behavior on the fitness model converges to that of the scale-free network, as expected. However, at some temperatures below the critical temprature of Bose- Einstein condensate phase on the fitness network, the complete condensation occurs as well for some values of δ> δ_c, which is impossible on scale-free network according to the criterion.     

Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms

The mutation operation is critical to the success of genetic algorithms since it diversifies the search directions and avoids convergence to local optima. The earliest genetic algorithms use only one mutation operator in producing the next generation. Each problem, even each stage of the genetic process in a single problem, may require appropriately different mutation operators for best results. Determining which mutation operators should be used is quite difficult and is usually learned through experience or by trial-and-error. This paper proposes a new genetic algorithm, the dynamic mutation genetic algorithm, to resolve these difficulties. The dynamic mutation genetic algorithm simultaneously uses several mutation operators in producing the next generation. The mutation ratio of each operator changes according to evaluation results from the respective offspring it produces. Thus, the appropriate mutation operators can be expected to have increasingly greater effects on the genetic process. Experiments are reported that show the proposed algorithm performs better than most genetic algorithms with single mutation operators.     

Error Thresholds in Single-Peak Gaussian Distributed Fitness Landscapes

Based on the Eigen and Crow-Kimura models with a single-peak fitness landscape, we propose the fitness values of all sequence types to be Gausslan distributed random variables to incorporate the effects of the fluctuations of the fitness landscapes （noise of environments） and investigate the concentration distribution and error threshold of quasispecies by performing an ensemble average within this theoretical framework. We find that a small fluctuation of the fitness landscape causes only a slight change in the concentration distribution and error threshold, which implies that the error threshold is stable against small perturbations. However, for a sizable fluctuation, quite different from the previous deterministic models, our statistical results show that the transition from quasi-species to error catastrophe is not so sharp, indicating that the error threshold is located within a certain range and has a shift toward a larger value. Our results are qualitatively in agreement with the experimental data and provide a new implication for antiviral strategies.