s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

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

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism ...

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...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different Genetic Fuzzy Rule-Based Systems, i. e., evolutionary algorithm-based processes to automatically design Fuzzy Rule-Based Systems by learning and/or tuning the Fuzzy Rule Base, following the same generic structure and able to cope with problems of different nature. A specific evolutionary learning process obtained from the paradigm proposed to design unconstrained approximate Mamdani-type Fuzzy RuleBased Systems will be introduced, and its accuracy in the solving of a real-world Electrical Engineering problem will be analyzed.

Genetic Algorithms and Evolution Strategies are combined in order to build a multistage Hybrid Evolutionary Algorithm for learning constrained Approximate Mamdani-type Knowledge Bases from examples. The Genetic Algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed Fuzzy Rule-Based Systems. The proposed Genetic Fuzzy Rule-Based System is used to solve an Electrical Engineering problem and the results obtained are compared with other methods presenting different characteristics.

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. The performance of the method proposed is shown by measuring the accuracy of the Fuzzy Logic Controllers designed in the modeling of two three-dimensional control surfaces derived from two mathematical functions presenting different characteristics. The results obtained by a method based on the Wang and Mendel's Knowledge Base generation process are also shown, allowing to compare both processes.

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 ...

Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for Fuzzy Logic. Descriptive Mamdani-type Fuzzy Rule-Based Systems (FRBSs), the ones used to perform this task, provide a human-readable description of the model in the form of linguistic rules, which is a desirable characteristic in many problems. Unfortunately, interpretability and accuracy are contradictory requirements in the field of modeling. Due to this reason, in many cases the linguistic model designed is not accurate to a sufficient degree and has to be discarded and replaced by other less interpretable but more accurate models (fuzzy models generated by means of TSK-type or approximate Mamdani-type FRBSs, neural models, black-box models, etc.). In this paper we are going to propose an approach for designing qualitative models which are accurate to a high degree and may be suitably interpreted. This approach will be based on two main assumptions related to the interpolative reason...

This paper has been partially supported by CICYT PB96-0778 2 Oscar Cord'on and Francisco Herrera (GAs), Evolution Strategies (ESs), Evolutionary Programming (EP), and Genetic Programming (GP). In this chapter, we make use the first two types of EAs. The most well known EAs are the GAs, i.e., search algorithms that use operations found in natural genetics to guide the search in complex search spaces. GAs are have been theoretically and empirically proven to have robust and computationally efficient search capabilities. They also have been demonstrated to be a powerful tool for automating the construction of the fuzzy rule bases, since learning and self-organization may be considered in a lot of cases as optimization and/or efficient search problems. The GAs based approaches used in the context of automating and optimizing the construction of fuzzy rule bases are known under the name of genetic fuzzy systems