Abstract: Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the field and we conclude by showing several new trends and new research directions in this field.

Abstract: Since the introduction of the extension principle by Zadeh, the arithmetic of fuzzy numbers has gained importance both from the theoretical and the practical points of view. For the former, many works were accomplished on the topological level as well as on the parametrization level in order to improve the foundation of the theory and to simplify the performance of different combination operations. For the latter, in many practical applications, the need for a permanent switch from a fuzzy representation to a numerical representation is patent. This transformation is usually carried out by the defuzzification process. This paper addresses some theoretical results about some invariance properties concerning the relationships between the defuzzification outcomes and the arithmetic of fuzzy numbers. One of the benefits of such analysis is the fact that when the matter is the determination of the defuzzified value pertaining to the result of some manipulation of fuzzy quantities, the explicit determination of the resulting fuzzy set (or distribution) can be obviated, while the process may be restricted to a standard computation over single values corresponding to defuzzified initial inputs.

Abstract: Part I: Keynote Papers.Part II: Intelligent Control.Part III: Classification, Clustering and Optimization.Part IV: Image and Signal Processing.Part V: Agents, Multimedia and Internet.Part VI: Theoretical Advances and New Paradigms.Part VII: Prediction, Design and Diagnosis.

Abstract: Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamically-constructed, feedback fuzzy neural controller (DCF-FNC) from the input–output data of the process and a reference model, for direct model reference adaptive control (MRAC) to deal with such problems. The effectiveness of our approach is demonstrated by simulation results on a real-world example of cold mill thickness control and is compared with the performances of the conventional PID controller and the cascade correlation neural network (CCN). Exploiting the advantage of intelligent adaptive control, both the CCN and our DCF-FNC significantly increases the control precision and robustness, compared to the linear PID controller, with our DCF-FNC giving the best results in terms of both accuracy and compactness of the controller, as well as being less computationally intensive than the CCN. We argue that our DCF-FNC feedback controller with both structure and parameter learning can provide a computationally efficient solution to control of many real-world multivariable nonlinear processes in presence of disturbances and noise.

Abstract: 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.

Abstract: We present two types of fuzzy solutions to linear systems of first order differential equations having fuzzy initial conditions. The first solution, called the extension principle solution, fuzzifies the crisp solution and then checks to see if its α-cuts satisfy the differential equations. The second solution, called the classical solution, solves the fuzzified differential equations and then checks to see if the solution always defines a fuzzy number. Three applications are presented: (1) predator–prey models; (2) the spread of infectious diseases; and (3) modeling an arms race.

Abstract: The chapter describes inductive machine learning methods for generating hypotheses about given training data. It focuses on hybrid algorithms that generate hypotheses in the form of production ifthenrules, which constitute the model of the data.

Abstract: We consider finite Markov chains where there are uncertainties in some of the transition probabilities. These uncertainties are modeled by fuzzy numbers. Using a restricted fuzzy matrix multiplication we investigate the properties of regular, and absorbing, fuzzy Markov chains and show that the basic properties of these classical Markov chains generalize to fuzzy Markov chains.

Abstract: The paper presents a prototype of Fuzzy e-Negotiation Agents (FeNAs) for autonomous multi-issue negotiation in e-commerce. It considers negotiation as a form of distributed decision making in the presence of limited common knowledge and imprecise/soft constraints that can be modeled as a distributed fuzzy constraint satisfaction problem (DFCSP). FeNAs incorporate the principles of utility theory within DFCSPs and use fuzzy constraint-based reasoning in order to find a consensus that maximizes the agent's utility and the level of its fuzzy constraint satisfaction subject to its acceptability by other agents. The paper presents aspects of problem representation and negotiation mechanisms used by FeNAs in the context of DFCSPs. An overview of FeNAs is provided and some capabilities for automated multi-issue negotiation are illustrated with two scenarios of e-commerce trading.

Abstract: Closure operators (and related structures) are investigated from the point of view of fuzzy set theory. The paper is a follow up to [7] where fundamental notions and result have been established. The present approach generalizes the existing approaches in two ways: first, complete residuated lattices are used as the structures of truth values (leaving the unite interval [0,1] with minimum and other t-norms particular cases); second, the monotony condition is formulated so that it can reflect also partial subsethood (not only full subsethood as in other approaches). In this paper, we study relations induced by fuzzy closure operators (fuzzy quasiorders and similarities); factorization of closure systems by similarities and by so-called decrease of logical precision; representation of fuzzy closure operators by (crisp) closure operators; relation to consequence relations; and natural examples illustrating the notions and results.

Abstract: This paper deals with the problem of rule interpolation and rule extrapolation for fuzzy and possibilistic systems. Such systems are used for representing and processing vague linguistic If-Then-rules, and they have been increasingly applied in the field of control engineering, pattern recognition and expert systems. The methodology of rule interpolation is required for deducing plausible conclusions from sparse (incomplete) rule bases. The interpolation/extrapolation method which was proposed for one-dimensional input space in [4] is extended in this paper to the general n-dimensional case by using the concept of aggregation operators. A characterization of the class of aggregation operators with which the extended method preserves all the nice features of the one- dimensional method is given.

Abstract: We present a study of the role of user profiles using fuzzy logic in web retrieval processes. Flexibility for user interaction and for adaptation in profile construction becomes an important issue. We focus our study on user profiles, including creation, modification, storage, clustering and interpretation. We also consider the role of fuzzy logic and other soft computing techniques to improve user profiles. Extended profiles contain additional information related to the user that can be used to personalize and customize the retrieval process as well as the web site. Web mining processes can be carried out by means of fuzzy clustering of these extended profiles and fuzzy rule construction. Fuzzy inference can be used in order to modify queries and extract knowledge from profiles with marketing purposes within a web framework. An architecture of a portal that could support web mining technology is also presented.

Abstract: We discuss a new paradigm, called active learning, for supervised learning that aims at improving the efficiency of neural network training procedures. The starting point for active learning is the observation that the traditional approach of randomly selecting training samples leads to large, highly redundant training sets. This redundancy is not always desirable. Especially if the acquisition of training data is expensive, one is rather interested in small, information training sets. Such training sets can be obtained if the learner is enabled to select those training data that he or she expects to be most informative. In this case, the learner is no longer a passive recipient of information but takes an active role in the selection of the training data.

Abstract: Existing fuzzy relational equations (FRE) typically possess an evident single-level structure, where no consequence part of the rule being modeled, is used as a fact to another rule. Corresponding to multistage fuzzy reasoning, a natural extension of traditional fuzzy relational systems (FRS) is to introduce some intermediate levels of processing governed by enhanced FRE's so that the structure resulted becomes multilevel or multistage. Three basic multilevel FRS structures, namely, incremental, aggregated, and cascaded, are considered in this paper and they correspond to different reasoning mechanisms being frequently used by human beings in daily life. While the research works on multilevel FRS are sparse and our ability to solve a system of multilevel FRE's in a purely analytical manner is very limited, we address the identification problem from an optimization approach and introduce three fuzzy neural models. The proposed models consist of single-level FRS modules that are arranged in different hierarchical manners. Each module can be realized by Lin and Lee's fuzzy neural model for implementing the Mamdani fuzzy inference. We have particularly addressed the problem of how to distribute the input variables to different (levels of) relational modules for the incremental and aggregated models. In addition, the new models can learn a complete multistage fuzzy rule set from stipulated data pairs using structural and parameter learning. The effectiveness of the multilevel models has been demonstrated through various benchmarking problems. It can be generally concluded that the new models are distinctive in learning, generalization, and robustness.

Abstract: Fuzzy rules are conditional pieces of knowledge which can either express constraints on the set of values which are left possible for a variable, given the values of other variables, or accumulate tuples of feasible values. The first type are implicative rules, while the second are based on conjunctions. Consequences of this view on inference and interpolation between sparse rules are presented.

Abstract: The process of ranking (scoring) has been used to make billions of financing decisions each year serving an industry worth hundreds of billion of dollars. To a lesser extent, ranking has also been used to process hundreds of millions of applications by U.S. Universities resulting in over 15 million college admissions in the year 2000 for a total revenue of over $250 billion. College admissions are expected to reach over 17 million by the year 2010 for total revenue of over $280 billion. In this paper, we will introduce fuzzy query and fuzzy aggregation as an alternative for ranking and predicting the risk for credit scoring and university admissions, which currently utilize an imprecise and subjective process. In addition we will introduce the BISC Decision Support System. The main key features of the BISC Decision Support System for the internet applications are (1) to use intelligently the vast amounts of important data in organizations in an optimum way as a decision support system and (2) to share intelligently and securely company's data internally and with business partners and customers that can be process quickly by end users.

Abstract: In this paper we define the lower and upper approximations of fuzzy sets in a group with respect to a fuzzy normal subgroup and study their product properties. We introduce the notion of a rough fuzzy subgroup of a group with respect to a fuzzy normal subgroup and study the relation between the fuzzy subgroup and the rough fuzzy subgroup. We also study the homomorphic properties of the rough fuzzy subgroups.

Abstract: This paper presents a fuzzy goal programming procedure for solving linear bilevel programming problems. The concept of tolerance membership functions for measuring the degree of satisfactions of the objectives of the decision makers at both the levels and the degree of optimality of vector of decision variables controlled by upper-level decision maker are defined first in the model formulation of the problem. Then a linear programming model by using distance function to minimize the group regret of degree of satisfactions of both the decision makers is developed. In the decision process, the linear programming model is transformed into an equivalent fuzzy goal programming model to achieve the highest degree (unity) of each of the defined membership function goals to the extent possible by minimizing their deviational variables and thereby obtaining the most satisfactory solution for both the decision makers. To demonstrate the approach, a numerical example is solved and compared the solution with the solutions of other two fuzzy programming approaches [11,12 ] studied previously.

Abstract: In recent years, available audio corpora are rapidly increasing from fast growing Internet and digital libraries. How to classify and retrieve sound files relevant to the user's interest from large databases is crucial for building multimedia web search engines. In this paper, content-based technology has been applied to classify and retrieve audio clips using a fuzzy logic system, which is intuitive due to the fuzzy nature of human perception of audio, especially audio clips with mixed types. Two features selected from various extracted features are used as input to a constructed fuzzy inference system (FIS). The outputs of the FIS are two types of hierarchical audio classes. The membership functions and rules are derived from the distributions of extracted audio features. Speech and music can thus be discriminated by the FIS. Furthermore, female and male speech can be separated by another FIS, whereas percussion can be distinguished from other music instruments. In addition, we can use multiple FISs to form a “fuzzy tree” for retrieval of more types of audio clips. With this approach, we can classify and retrieve generic audios more accurately, using fewer features and less computation time, compared to other existing approaches.

Abstract: It is observed that landing performance is the most typical phase of an aircraft performance. During landing operation the stability and controllability are the major considerations. To achieve a safe landing, an aircraft has to be controlled in such a way that its wheels touch the ground comfortably and gently within the paved surface of the runway.

Abstract: Retrieving relevant information is a crucial component of cased-based reasoning systems for Internet applications such as search engines. The task is to use user-defined queries to retrieve useful information according to certain measures. Even though techniques exist for locating exact matches, finding relevant partial matches might be a problem. It may not be also easy to specify query requests precisely and completely - resulting in a situation known as a fuzzy-querying. It is usually not a problem for small domains, but for large repositories such as World Wide Web, a request specification becomes a bottleneck. Thus, a flexible retrieval algorithm is required, allowing for imprecise or fuzzy query specification or search.

Abstract: In this paper some research trends in the field of Information Retrieval are presented. The focus is on the definition of flexible systems, i.e. systems that can represent and manage the vagueness and uncertainty which is characteristic of the process of information searching and retrieval. In this paper the application of soft computing techniques is considered, in particular fuzzy set theory.

Abstract: In speaker verification, a claimed speaker's score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker's and the impostors' likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased normalisation method to find a better score which can reduce that error. Experiments performed on the TI46 and the ANDOSL speech corpora show better results for the proposed method.

Abstract: A granular neural Web-based stock prediction agent is developed using the granular neural network (GNN) that can discover fuzzy rules. Stock data sets are downloaded from www.yahoo.com website. These data sets are inserted into the database tables using a java program. Then, the GNN is trained using sample data for any stock. After learning from the past stock data, the GNN is able to use discover fuzzy rules to make future predictions. After doing simulations with six different stocks (msft, orcl, dow, csco, ibm, km), it is conclusive that the granular neural stock prediction agent is giving less average errors with large amount of past training data and high average errors in case of fewer amounts of past training data. Java Servlets, Java Script and jdbc are used. SQL is used as the back-end database. The performance of the GNN algorithm is compared with the performance of the BP algorithm by training the same set of data and predicting the future stock values. The average error of the GNN is less than that of BP algorithm.

Abstract: A visual language and a multimedia environment supporting the language are considered. The language is explained through describing a visual program for solving partial differential equations by a multigrid method. The environment is based on a database of self-explanatory components in a “film” format and a film management system for searching, editing, composing and other manipulations with components. The visual program presented is only one view of a self-explanatory component. In fact, it can also be watched in dynamics and from other points of view for better understanding the method features. The goal of the visual program example is to show that visual programs can be physically much smaller than the text they replace and much easier for understanding and modifying.

Abstract: In this contribution, we will study the influence of the three main components of Best-Worst Ant System: the best-worst pheromone trail update rule, the pheromone trail mutation and the restart. Both the importance of each of them and the fact whether all of them are necessary will be analyzed. The performance of different variants of this algorithm will be tested when solving different instances of the TSP.

Abstract: This paper describes the concept of fuzzy regression analysis based on genetic algorithms. It is shown that the performance of fuzzy regression models may be improved and fuzzy modeling technique can be simplified by incorporating genetic algorithms into regression analysis procedure. The effectiveness of the proposed approach is illustrated through simulation of fuzzy linear regression model obtained by other authors and comparison of the results. The paper further demonstrates the applications of the approach to the manufacturing and business problems.

Abstract: In this paper we present a new multilevel information sharing strategy within a swarm to handle single objective, constrained and unconstrained optimization problems. A swarm is a collection of individuals having a common goal to reach the best value (minimum or maximum) of a function. Among the individuals in a swarm, there are some better performers (leaders) those that set the direction of search for the rest of the individuals. An individual that is not in the better performer list (BPL) improves its performance by deriving information from its closest neighbor in BPL. In an unconstrained problem, the objective values are the performance measures used to generate the BPL while a multilevel Pareto ranking scheme is implemented to generate the BPL for constrained problems. The information sharing strategy also ensures that all the individuals in the swarm are unique as in a real swarm, where at a given time instant two individuals cannot share the same location. The uniqueness among the individuals result in a set of near optimal individuals at the final stage that is useful for sensitivity analysis. The benefits of the information sharing strategy within a swarm are illustrated by solving two unconstrained problems with multiple equal and unequal optimum, a constrained optimization problem dealing with a test function and a well studied welded beam design problem.