一种集成遗传算法与模糊推理的粗糙集数据分析算法

粗糙集数据分析的主要优点在于它不要求任何关于被处理数据的先验或额外的知识,文章利用其对数据库进行分析计算,自动获取数据库在各个层次上的规则集。在保证量化后的数据库具有最大一致性的前提下,利用遗传算法求取连续属性值的最优量化区间个数及各个区间分点值。同时将量化区间进行模糊化,将清晰规则集转化为模糊规则集,利用模糊推理进行决策以提高鲁棒性。通过对UCI中几个数据库的测试验证了所提出算法的有效性。     

Global web server load balancing using evolutionary computational techniques

 As user load on web servers becomes more globalised, duplicated regional mirroring is being seen as an increasingly expensive solution to meeting regional peak demand. Alternative solutions are being explored by looking at dynamic load balancing using distributed intelligent middleware to re-route traffic from busy regions to quieter ones as global load patterns change over a 24 h cycle. The techniques used can also be employed under fault and planned maintenance conditions. One such solution, providing the load balancing via reconfigurable dynamic proxy servers, is seen as `unobtrusive' in that it works with standard web browsers and web server technology. The technique employs an evolutionary algorithm to perform combinatorial optimisation against a dynamic performance predicting model. This paper describes this solution, focussing on issues such as algorithm tuning, scalabilty and reliability. A prototype system is currently being trialled within the Systems Integration Department at British Telecommunications Research Labs, Adastral Park and is the subject of several BT held patents.     

基于模糊集截集的模糊粗糙集模型

基于L.A.Zadeh模糊集的截集的概念给出了论域U上任意模糊子集的上、下近似的刻画,得到了基于模糊集的截集的粗糙集模型,亦即模糊粗糙集,实现了用论域U中的模糊集近似论域上的任意模糊集,进一步推广了Z.Pawlak粗糙集模型,扩展了粗糙集的应用范围。最后,研究了其基本性质以及其与其他粗糙集模型的关系。     

Soft computing and fuzzy logic

What is soft computing? What is fuzzy computing? What is the relationship between them? This paper intends to provide clear answers to these questions. We focus on exploring the notions of the fuzzy coordinate system and the related transformations between qualitative and quantitative information. These notions are considered to be the core ideas of fuzzy computing. Then the three novel theories of fuzzy computing and soft computing developed by the first author of this paper, namely, the Falling Shadow Representation of fuzzy theory, the Factors Space theory and the Truth Value Flow Inference theory are introduced.     

An entropy construction inspired by fuzzy sets

 Fuzzy dynamics is considered as a model of a general algebraic scheme based on two binary operations fulfilling a very weak distributive law. The main result is the existence of the limit defining the entropy of the general dynamical system. Present address: Katedra matematiky Fakulty prírodnych vied UMB, Tajovského 40, SK-97 401 Banská Bystrica, Slovakia E-mail: riecan@fpv.umb.sk Dedicated to Prof. Ján Jakubík on the occasion of his 80th birthday Supported by grant VEGA 1/9056/02.     

A bit-masking oriented data structure for evolutionary operators implementation in genetic algorithms

 In the present paper a special bit-masking oriented data structure for an improved implementation of crossover and mutation operators in genetic algorithms is shown. The developed data structure performs evolutionary operators in two separate steps: crossover and mutation mask fill and a special boolean based function application. Both phases are optimized to reach a more efficient, fast and flexible genetic reproduction than standard implementations. The method has been powered adding a multi-layered, bit-masking oriented data structure and a boolean operation based control mixer, allowing special blended crossover operators obtained by superposition of the standard ones. Several examples of crossover schemes produced by these extended controls are presented. In addition, a special purpose crossover scheme, capable to process at the same time two distinct groups of design variables with separate crossover schemes is shown, in order to improve efficiency and convergence speed of some discrete/continuous optimization problems. Finally, to highlight further capabilities of the bit-masking approach, a special single-step version of an evolutionary direction operator is also illustrated.     

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

 Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.     

A new mutation operator for evolutionary airfoil design

A new mutation operator, ℳ _ijn , capable of operating on a set of adjacent bits in one single step, is introduced. Its features are examined and compared against those of the classical bit–flip mutation. A simple Evolutionary Algorithm, ℳ–EA, based only on selection and ℳ _ijn , is described. This algorithm is used for the solution of an industrial problem, the Inverse Airfoil Design optimization, characterized by high search time to achieve satisfying solutions, and its performance is compared against that offered by a classical binary Genetic Algorithm. The experiments show for our algorithm a noticeable reduction in the time needed to reach a solution of acceptable quality, thus they prove the effectiveness of the proposed operator and its superiority to GAs for the problem at hand.     

Fuzzy rough set theory for the interval-valued fuzzy information systems

The concept of the rough set was originally proposed by Pawlak as a formal tool for modelling and processing incomplete information in information systems, then in 1990, Dubois and Prade first introduced the rough fuzzy sets and fuzzy rough sets as a fuzzy extension of the rough sets. The aim of this paper is to present a new extension of the rough set theory by means of integrating the classical Pawlak rough set theory with the interval-valued fuzzy set theory, i.e., the interval-valued fuzzy rough set model is presented based on the interval-valued fuzzy information systems which is defined in this paper by a binary interval-valued fuzzy relations RF⁽ⁱ⁾(U×U) on the universe U. Several properties of the rough set model are given, and the relationships of this model and the others rough set models are also examined. Furthermore, we also discuss the knowledge reduction of the classical Pawlak information systems and the interval-valued fuzzy information systems respectively. Finally, the knowledge reduction theorems of the interval-valued fuzzy information systems are built.     

Fuzzy configuration of matching runtime implementation strategies

 With applications currently growing in complexity and range, increasing numbers of configuration problems are arising in compilers. Already many software systems offer multiple specialized implementation strategies and substrategies, differing in terms of applicability and/or cost, depending on the application context. Configurations then have to be created from the different strategies available in accordance with the application characteristics, the global optimization objective, and potential constraints on the strategies' combinability. In many cases, this results in a combinatorial, i.e., discrete, optimization problem. Proper solutions for automating the configuration while limiting the complexity of the solution search are still being sought. We address here the field of parallel/distributed processing and the configuration of runtime implementation strategies, such as for communication or dynamic load balancing. We present a rule-based approach, integrating fuzzy methodologies for the classification of application characteristics and for gradual selection preference in rules. In this way we exploit available knowledge about the correlation of the problem and solution space, and apply soft computing methods to obtain an approximate, rather than perfect, solution approach, thus helping to limit the configuration complexity. Our approach extends standard fuzzy inference by a multistage organization, and – with proper organization of rules, characteristics and strategies – performs hierarchical fuzzy inference. The approach is demonstrated on concrete configuration examples in parallel compilers.     

粒计算研究综述

粒计算（granular computing）是当前计算智能研究领域中模拟人类思维和解决复杂问题的新方法．它覆盖了所有有关粒度的理论、方法和技术，是复杂问题求解、海量数据挖掘、模糊信息处理的有效工具．首先回顾了粒计算研究和发展状况，介绍了粒计算的基本组成和问题，综述了粒计算的基本模型和方法，并讨论了它们之间的相互关系，最后探讨了构建统一的粒计算模型、复杂问题空间的粒化、粒层之问的转换、高效的粒计算方法、新的粒计算模型、动态粒计算模型、自主粒计算模型、粒计算方法的模糊化以及粒计算模型的应用和推广等几个方面的关键问题．     

基于粗集的模糊属性值信息系统的知识获取

文章研究了一类属性取模糊值的不完备信息系统的知识获取方法。首先给出了信息系统的一种表示方法,同时采用模糊集思想,构造了这类系统的模糊不可分辨关系,并研究了这类信息系统的知识获取算法,并以一个实例,说明了这种算法的有效性。     

不同目标函数对用GA寻优Fuzzy控制规则的影响

本文首先简单介绍了遗传算法和模糊控制自寻优,然后给出了用遗传算法对模糊控制规则进行了自寻优的方法,并通过仿真比较不了同目标函数对用遗传算法寻优模糊控制规则的影响,得出了一些结论。     

Structured population-based incremental learning

 We investigate a recently developed abstraction of genetic algorithms (GAs) in which a population of GAs in any generation is represented by a single vector whose elements are the probabilities of the corresponding bit positions being equivalent to 1. The process of evolution is represented by learning the elements of the probability vector; the method is clearly linked to the artificial neural network (ANN) method of competitive learning. We use techniques from ANNs to extend the applicability of the method to non-static problems, to multi-objective criteria, to multi-modal problems and to creating an order on a set of sub-populations.     

模糊证据理论及其在信息融合中的应用

为了使证据理论能更加有效地应用,把证据理论向模糊集推广,利用模糊集的隶属函数提出一种构造证据理论中的基本概率赋值函数的方法,实现了模糊理论和证据理论的有效结合。不但有效地解决了证据理论中的基本概率赋值函数的不易确定问题,而且由于证据理论应用于实际更加方便和有效,融合结果也更加合理。     

基于遗传算法的二级倒立摆模糊控制器研究

倒立摆系统本身的不稳定性为系统的平衡提出了难题,也因此成为自动控制实验中验证控制算法优劣的极好的实验装置.本文利用把模糊控制和遗传算法结合起来,用遗传算法对模糊控制器的三角形隶属度函数的宽度和顶点进行自动调整,,克服了一般模糊控制设计中模糊变量的隶属度和比例、量化因子的选取通常靠经验来获取的不足,从而达到优化设计模糊控制器提高其自适应控制能力的目的,改善了控制效果.     

Fuzzy control of complex systems

 One of the biggest challenges of any control paradigm is being able to handle large complex systems. A system may be called large-scale or complex, here, if its dimension (order) is so high and its model (if available) is nonlinear, interconnected with uncertain information flow such that classical techniques of control theory cannot easily handle the system. From a control theoretical point of view, fuzzy logic has been intermixed with all the important aspects of systems theory - modeling, identification, analysis, stability, synthesis, filtering, and estimation. However, the application of fuzzy control to large-scale complex systems is not a trivial task by any means. For such systems the size of the rule base in a typical fuzzy control architecture will be nearly infinite. In this paper an attempt is made to break some new ground on the applications of fuzzy control to complex systems. A new rule base reduction approach is suggested to manage large inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve system’s goals. The technique has been implemented on an SGS Thomson W.A.R.P. chip for an inverted pendulum with wine-balancing application.     

关于Vague集的模糊熵

由于Vague集是Zadeh's模糊集的一个扩展,为计算Vague集的模糊熵,有学者提出将Vague集转化为模糊集,然后借用模糊集有关熵的计算方法来讨论它们。该文首先给出反例说明Li's(2003)的方法在某些情况下和基于模糊集的Vague集模糊熵定义不一致。在指出Vague集的模糊性主要来自未知信息和不确定性信息的基础上,提出了一个基于非模糊集的Vague集模糊熵公理化定义,给出了该类模糊熵的计算公式,最后通过定理证明了它确实同时考虑到了影响Vague集模糊熵的两个因素。     

基于动态模糊模型的稳定模糊状态反馈控制器的遗传算法实现

分别从级数和Ｌｙａｐｕｎｏｖ稳定的角度分析了基于动态模糊模型的离散模糊系统的稳定性，得到了稳定性的充分条件。并在此基础上给出了对模糊状态反馈控制器参数进行优化设计的一种遗传算法实现策略。仿真例子表明了其优越性。