Nowadays Fuzzy Logic Controllers have been succesfully applied to a wide range of engineering control processes. Several tasks have to be performed in order to design an intelligent control system of this kind for a concrete application. One of the most important and difficult ones is the extraction of the expert known knowledge of the controlled system. The aim of this paper is to present an evolutionary process based on genetic algorithms and evolution strategies for learning the Fuzzy Logic Controller Knowledge Base from examples in three different stages. The process allows us to generate two different kinds of Knowledge Bases, descriptive and approximative ones, depending on the scope of the fuzzy sets giving meaning to the fuzzy control rule linguistic terms, taking preliminary linguistic variable

This paper proposes using fuzzy logic techniques to dynamically control parameter settings of genetic algorithms (GAs). We describe the Dynamic Parametric GA: a GA that uses a fuzzy knowledge-based system to control GA parameters. We then introduce a technique for automatically designing and tuning the fuzzy knowledgebase system using GAs. Results from initial experiments show a performance improvement over a simple static GA. One Dynamic Parametric GA system designed by our automatic method demonstrated improvement on an application not included in the design phase, which may indicate the general applicabilityof the Dynamic Parametric GA to a wide range of applications.

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable Genetic Algorithms. Keywords: Fuzzy logic control systems, learning, genetic algorithms. 1 Introduction Fuzzy rule based systems have been shown to be an important tool for modelling complex systems, in which due to the complexity or the imprecision, classical tools are unsuccessful. Fuzzy Logic Controllers (FLCs) are now considered as one of the most important applications of the fuzzy rule based systems. The e...

Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the socalled genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples. Keywords: Fuzzy logic, fuzzy rules, genetic algorithms, machine learning. 1 Introduction Genetic Algorithms (GAs) are search algorithms that use operations found in natural genetics to guide the trek through a search space. GAs are theoretically and empirically proven to provide robust search capabilities in complex spaces, offering a valid approach to problems requiring efficient and effective searching. Much of the interest in GAs is due to the fact that they provide a...

Nowadays, Fuzzy Rule-Based Systems are successfully applied to many different real-world problems. Unfortunatelly, relatively few well-structured methodologies exist for designing them and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. TSK Fuzzy Rule-Based Systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK Fuzzy Rule-Based Systems from examples combining a generation stage based on a (¯; )-Evolution Strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary Knowledge Base, and a refinement stage, in which both the antecedent and consequent parts of the fuzzy rules in this previous Knowledge Base are adapted by a hybrid evolutionary process composed of a Genetic Algorithm and an Evolution Strategy to obtain the ...

Two ingredients of soft computing, evolutionary computing and fuzzy logic can be combined in a way that makes them benefit from one another. An evolutionary algorithm can evolve fuzzy systems, while fuzzy logic can be used to control evolution to speed up convergence to a global optimum and escape from local optima. Besides, concepts that are inherent in the workings of evolutionary algorithms, like fitness, can be fuzzified, thus taking advantage of a tolerance for imprecision in order to save computational resources. These ideas are demonstrated in an evolutionary algorithm for fuzzy controller synthesis and optimization. 1 Introduction Integration of evolutionary algorithm and fuzzy logic can happen in three complementary forms. The most obvious one exploits the optimum searching ability of evolutionary algorithms to synthesize and optimize a fuzzy system, The author was sponsored in part by SGSThomson Microelectronics such as a fuzzy rule set or a neuro-fuzzy network. This com...

The main aim of this paper is to present an evolutionary process for designing TSK Fuzzy Rule-Based Systems based on the combination of an inductive algorithm that decides the number of rules forming the Knowledge Base, and a (¯; )-Evolution Strategy that determines their consequent parameters. Some aspects make this process different from others proposed till now: the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolution Strategies that allows us to speed up the search process, obtaining good solutions more quickly. The performance of the method proposed is shown by measuring the accuracy of the TSK Fuzzy Rule-Based Systems designed in the fuzzy modeling of two three-dimensional surfaces and comparing it with two Mamdani-type ones, generated by using inductive and evolutionary design pro...

This paper illustrated an evolutionary algorithm which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community.

Introduction The application of fuzzy logic to control problems has become common practice, particularly in Eastern Asia and Europe, and interest has been growing in the U.S. as well. A reason for this success is that it is easier for human experts to express their knowledge about a system in the form of rules that put linguistic variables in relation with one another. After a little tuning, controllers built in such a manner can work as well as any controller obtained following the guidelines of classical control theory, except that much less effort and expertise is needed. In some cases, for particularly complex systems whose dynamics are highly non-linear, applying classical control theory becomes too difficult, and fuzzy control is the only viable resort. After the first successful applications of fuzzy logic to control problems, speculation arose as to whether it would be possible to develop an automated method to either synthesize the rules of a fuzzy controlle

: This paper presents DEE, the Distributed Evolutionary Engine, a complete framework for the off-line tuning of fuzzy-logic based software components using parallel adaptation algorithms. The system was implemented on a high-speed network of workstations by means of a general-purpose task distribution tool. After the description of DEE's architecture, the tuning of fuzzy software components is discussed as an alternative to maintenance, and some encouraging experimental results are described. 1. Introduction The idea of using evolutionary algorithms to tune parameters of fuzzy software components is relatively recent. The first attempts in this direction were aimed to the synthesis and optimization of fuzzy controllers (Karr 1991, Thrift 1991). Besides control, another area of research is data mining, where evolutionary algorithms are used to optimize queries. This optimization task becomes particularly interesting when queries are vague, database indexing is fuzzy and the data themse...