In this paper, we present a classiied review of the bibliography on the combination fuzzy logic|genetic algorithms. The classiication is developed collecting the papers making reference to an speciic fuzzy logic area.

A classified review on the combination fuzzy logic–genetic algorithms bibliography: 1989–1995

This bibliography contains references to contributions published in journals, conference proceeding, books, and theses that deal with genetic algorithms and fuzzy techniques. The references have been collected from the author’s general genetic algorithm bibliography, which contains nearly 7000 references when this special fuzzy collection was compiled.

An indexed bibliography of genetic algorithms with fuzzy logic

An Indexed Bibliography of Genetic Algorithms in Control

An Indexed Bibliography of Genetic Algorithms with Fuzzy Systems

Similar to what can be found all over the world, Evolutionary Computation (EC) has been receiving an increasing attention in Brazil in the last decade. It is possible to find several works on EC, both in theory and applications, produced by Brazilian researchers. In this paper, a review of the works on EC produced in Brazil indexed in two important databases until April 2006 is presented. The papers are classified, and the results are discussed. Most of works on EC produced in Brazil are applications, mainly in the areas where the use of optimization methods is traditional. The EC terminology employed in Brazil is discussed in this paper too. One can observe that different terms are used to describe the same elements of EC.

Evolutionary Computation in Brazil: A Review of the Literature in Two Databases

Fuzzy controllers are based on a rule set that describe the strategy applied to control a plant. Some rules are obvious but in many cases to choose a set of rules is not a very easy task. Almost all fuzzy consumer products on the market rely on rules supplied by a human expert. Tuning the rules is a lengthy and cumbersome process, and some effort has been made toward the automation of this process. Self-organizing-controllers (SOC) and neural networks are examples. Another approach is found in genetic algorithms, which make it possible to explore a great range of solutions. The advantage is that little has to be known about the plant to be controlled and also about the best set of rules. When applied to servo-systems like positioning systems for NC machine tools, some obvious rules may, or even have to, be used, but a final set may be implemented by the algorithm. In this paper, some experimental efforts to combine the power of genetic algorithms and the practical applications of fuzzy controllers, are described. >

C.A. Martin

Papers

Experiments in fuzzy control using genetic algorithms