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Tackling realcoded genetic algorithms: operators and tools for the behavioural analysis
 Arti Intelligence Reviews
, 1998
"... Abstract. 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 prin ..."
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Cited by 172 (26 self)
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Abstract. 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 realcoded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared. Key words: genetic algorithms, real coding, continuous search spaces Abbreviations: GAs – genetic algorithms; BCGA – binarycoded genetic algorithm; RCGA – realcoded genetic algorithm
Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems
 IEEE TRANS. SYSTEMS, MAN CYBERNETICS—PART B: CYBERNET
, 1999
"... We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be vi ..."
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Cited by 88 (10 self)
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We examine the performance of a fuzzy geneticsbased machine learning method for multidimensional pattern classification problems with continuous attributes. In our method, each fuzzy if–then rule is handled as an individual, and a fitness value is assigned to each rule. Thus, our method can be viewed as a classifier system. In this paper, we first describe fuzzy if–then rules and fuzzy reasoning for pattern classification problems. Then we explain a geneticsbased machine learning method that automatically generates fuzzy if–then rules for pattern classification problems from numerical data. Because our method uses linguistic values with fixed membership functions as antecedent fuzzy sets, a linguistic interpretation of each fuzzy if–then rule is easily obtained. The fixed membership functions also lead to a simple implementation of our method as a computer program. The simplicity of implementation and the linguistic interpretation of the generated fuzzy if–then rules are the main characteristic features of our method. The performance of our method is evaluated by computer simulations on some wellknown test problems. While our method involves no tuning mechanism of membership functions, it works very well in comparison with other classification methods such as nonfuzzy machine learning techniques and neural networks.
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... 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 Proceed ..."
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Cited by 85 (10 self)
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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 GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Soft Computing: the Convergence of Emerging Reasoning Technologies
 Soft Computing
, 1997
"... The term Soft Computing (SC) represents the combination of emerging problemsolving 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 so ..."
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Cited by 64 (8 self)
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The term Soft Computing (SC) represents the combination of emerging problemsolving 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, realworld 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 backpropagationtype algorithms.
A Learning Process for Fuzzy Control Rules using Genetic Algorithms
, 1995
"... 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, expert ..."
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Cited by 56 (34 self)
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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 Tuning of Fuzzy Rule Deep Structures for Linguistic Modeling
 IEEE Trans. on Fuzzy Systems
, 2001
"... Tuning fuzzy rulebased systems for Linguistic Modeling is an interesting and widely developed task. It involves adjusting some of the components composing the knowledge base without completely redening it. To do that, as the fuzzy rule symbolic representations (known as fuzzy rule surface structure ..."
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Cited by 45 (13 self)
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Tuning fuzzy rulebased systems for Linguistic Modeling is an interesting and widely developed task. It involves adjusting some of the components composing the knowledge base without completely redening it. To do that, as the fuzzy rule symbolic representations (known as fuzzy rule surface structures) as the meaning of the involved membership functions (which together with the surface structures are known as fuzzy rule deep structures) may be modied. This contribution introduces a genetic tuning process for jointly tting these two components, i.e., whole deep structures. To adjust the symbolic representations, we propose to use linguistic hedges to perform slight modications keeping a good interpretability. To change the membership function meanings, two dierent ways considering basic or extended expressions are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two dierent levels of signicance. Keywords Fuzzy linguistic modeling, tuning, surface and deep structures, linguistic hedges. I.
MultiStage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach
, 1997
"... 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 contributio ..."
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Cited by 38 (11 self)
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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 geneticsbased machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multistage 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...
A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection
 IEEE Transactions on Fuzzy Systems
, 2007
"... Abstract—Linguistic fuzzy modeling allows us to deal with the modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the acc ..."
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Cited by 36 (22 self)
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Abstract—Linguistic fuzzy modeling allows us to deal with the modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the accuracy of the linguistic model arises when complex systems are modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to linguistic fuzzy modeling without losing the interpretability to a high level. In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining linguistic models with higher levels of accuracy while maintaining good interpretability. To do so, we consider a new rule representation scheme base on the linguistic 2tuples representation model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in highdimensional problems and its ability to cooperate with methods to remove unnecessary rules. Index Terms—Fuzzy rulebased systems, genetic algorithms, interpretability, linguistic 2tuples representation, rule selection, tuning. I.
A General Study on Genetic Fuzzy Systems
, 1993
"... 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 ..."
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Cited by 31 (14 self)
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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 17:36PAGE PROOFS for John Wiley & Sons Ltd (jwbook.sty v3.0, 1211995) A GENERAL STUDY ON GENETIC FUZZY SYSTEMS 3
Generating Fuzzy Rules from Examples using Genetic Algorithms
 Fuzzy Logic and Soft Computing
, 1995
"... 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 r ..."
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Cited by 30 (8 self)
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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 ...