. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some me...

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...

The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.

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 problem of generation desirable fuzzy rules is very important in the development of fuzzy systems. The purpose of this paper is to present a generation method of fuzzy control rules by learning from examples using genetic algorithms. We propose a real coded genetic algorithm for learning fuzzy rules, and an iterative process for obtaining a set of rules which covers the examples set with a covering value previously defined. Keywords: Fuzzy rules, learning, genetic algorithms. 1. Introduction Fuzzy rules based systems have been shown to be an important tool for modeling complex systems, where due to the complexity or the imprecision, classical tools are unsuccessful. In [19, 5] it was proved that fuzzy systems are universal approximators in the sense that for any continuous systems is possible to find a set of fuzzy rules able of approximating it with arbitrary accuracy. The problem is how to find the rules. There are different modes to derive them: - Based on Expert Experience ...

This paper presents an overview of the GFSs, showing the use of the GAs in the construction of the fuzzy logic controllers knowledge bases comprising the known knowledge about the controlled system. To achieve that, this paper is divided into 4 sections the first being this introduction. The section 2 introduces the fuzzy systems with a special attention to FLCs, while section 3 presents the GFSs. Some final remarks are made in section 4. cbook 2/9/1997 17:36---PAGE PROOFS for John Wiley & Sons Ltd (jwbook.sty v3.0, 12-1-1995) A GENERAL STUDY ON GENETIC FUZZY SYSTEMS 3

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...

In this paper we present different approaches to the problem of fuzzy rules extraction by using a combination of fuzzy clustering and genetic algorithms as the main tools. These approaches try to obtain a first approximation to the fuzzy rules that describe the system behavior that the numerical data indicate, without any assumption about the structure of the data using a fuzzy clustering technique. Subsequently these rules can be tuned using a genetic algorithm. Alternatively a similar genetic algorithm is proposed in order to generate and tune the fuzzy rules directly from the data, and their performance are compared. Keywords: fuzzy clustering; fuzzy modeling; genetic algorithms; hybrid systems 1 Introduction Several methods of extracting fuzzy rules from numerical data have been developed, but most of them assume the division of input variables in a number of fixed regions that could correspond with the linguistic variables in the domains of discourse [Ped84],[Wan92]. We are int...

We propose two fuzzy tools to improve the genetic algorithms behaviour. The use of fuzzy connectives to design crossover operators for real coded genetic algorithms (RCGA), and the use of fuzzy logic based systems for the dynamic control paprameters of RCGA based on diversity measures between chromosomes and alleles. 1. Introduction Genetic algorithms (GA) are search algorithms that use operations found in natural genetics to guide the trek through a search space. GA are theoretically and empirically proven to provide robust search in complex spaces, giving a valid approach to problems requiring efficient and effective search [Gol89]. A GA generally emploies three operators: selection, crossover, mutation. The selection operator is formulated after the darwinian principle of survival of the fittest, whereas the crossover and mutation operators have been inspired by the mechanisms of gene mutation and chromosome recombination found in biological genetics. Their computational role is to...

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