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 have proven to be a powerful tool for automating the Fuzzy Rule Base definition and, therefore, they have been widely used to design descriptive Fuzzy Rule-Based Systems for Qualitative Modeling. These kinds of genetic processes, called Genetic Fuzzy Rule-Based Systems, may be based on different genetic learning approaches, with the Michigan and Pittsburgh being the most well known ones. In this contribution, we briefly review another alternative, the Iterative Rule Learning approach, based on generating a single rule in each genetic run, and dealing with the problem of obtaining the best possible cooperation among the generated fuzzy rules. Two different ways for encouraging cooperation between rules in this genetic learning approach are presented, which are used in two different Genetic Fuzzy Rule-Based Systems based on it, SLAVE and MOGUL. Finally, the behaviour of these two processes in solving a qualitative modeling problem, the rice taste analysis, is analyse...

* * Corresponding author. Traditional genetic algorithms use only one crossover and one mutation operator to generate the next generation. The chosen crossover and mutation operators are critical to the success of genetic algorithms. Different crossover or mutation operators, however, are suitable for different problems, even for different stages of the genetic process in a problem. Determining which crossover and mutation operators should be used is quite difficult and is usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and mutation operators to generate the next generation. The crossover and mutation ratios change along with the evaluation results of the respective offspring in the next generation. By this way, we expect that the really good operators will have an increasing effect in the genetic process. Experiments are also made, with results showing the proposed algorithm performs better than the algorithms with a single crossover and a single mutation operator.

There are two possible ways for integrating Fuzzy Logic and Genetic Algorithms. One involves the application of Genetic Algorithms for solving optimization and search problems related with fuzzy systems. The another, the use of fuzzy tools and Fuzzy Logic-based techniques for modeling different Genetic Algorithm components and adapting Genetic Algorithm control parameters, with the goal of improving performance. The Genetic Algorithms resulting from this integration are called Fuzzy Genetic Algorithms. In this contribution, we tackle Fuzzy Genetic Algorithms by analyzing their definition based on the Zadeh's concept of Fuzzy Algorithms and the two different meanings as Fuzzy Logic may be viewed. We review different approaches, attempt to identify some open issues and summarize a few new promising research directions on the topic. Keywords: Fuzzy Logic, Genetic Algorithms, Fuzzy Algorithms, Fuzzy Genetic Algorithms. This research has been supported by CICYT TIC96-0778. 1 Introductio...

A genetic algorithm (GA) is a search and optimization method developed by mimicking the evolutionary principles and chromosomal processing in natural genetics. A GA begins its search with a random set of solutions usually coded in binary string structures. Every solution is assigned a fitness which is directly related to the objective function of the search and optimization problem. Thereafter, the population of solutions is modified to a new population by applying three operators similar to natural genetic operators---reproduction, crossover, and mutation. A GA works iteratively by successively applying these three operators in each generation till a termination criterion is satisfied. Over the past one decade, GAs have been successfully applied to a wide variety of problems, because of their simplicity, global perspective, and inherent parallel processing. In this paper, we outline the working principle of a GA by describing these three operators and by outlining an intuitive sketch ...

: 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...

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