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91
Niching Methods for Genetic Algorithms
, 1995
"... Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This ..."
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Cited by 208 (1 self)
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Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods. To achieve this purpose, it first develops a general framework for the modelling of niching methods, and then applies this framework to construct models of individual niching methods, specifically crowding and sharing methods. Using a constructed model of crowding, this study determines why crowding methods over the last two decades have not made effective niching methods. A series of tests and design modifications results in the development of a highly effective form of crowding, called determin...
Selecting fuzzy ifthen rules for classification problems using genetic algorithms
 IEEE TRANS. FUZZY SYST
, 1995
"... This paper proposes a geneticalgorithmbased method for selecting a small number of significant fuzzy ifthen rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives ..."
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Cited by 107 (18 self)
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This paper proposes a geneticalgorithmbased method for selecting a small number of significant fuzzy ifthen rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy ifthen rules. Genetic algorithms are applied to this problem. A set of fuzzy ifthen rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual is specified by the two objectives in the combinatorial optimization problem. The performance of the proposed method for training data and test data is examined by computer simulations on the iris data of Fisher.
Integrating design stages of fuzzy systems using genetic algorithms
, 1993
"... Abstract — This paper proposes an automaticfuzzy system design method that uses a Genetic Algorithm and integrates three design stages; our method determines membership functions, the number of fuzzy rules, and the ruleconsequent parameters at the same time. Because these design stages may not be in ..."
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Cited by 94 (1 self)
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Abstract — This paper proposes an automaticfuzzy system design method that uses a Genetic Algorithm and integrates three design stages; our method determines membership functions, the number of fuzzy rules, and the ruleconsequent parameters at the same time. Because these design stages may not be independent, it is important to consider them simultaneously to obtain optimal fuzzy systems. The method includes a genetic algorithm and a penalty strategy that favors systems with fewer rules. The proposed method is applied to the classic inverted pendulum control problem and has been shown to be practical through a comparison with another method. 1 1
Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms
 IEEE Transactions on Fuzzy Systems
, 1995
"... Abstract This paper examines the applicability of genetic algorithms (GA’s) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; howe ..."
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Cited by 85 (1 self)
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Abstract This paper examines the applicability of genetic algorithms (GA’s) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA’s have been used to develop both, it has been done serially, e.g., design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA’s are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules. I.
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 80 (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 79 (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...
Evolutionary Learning Of Fuzzy Rules: Competition And Cooperation
, 1996
"... We discuss the problem of learning fuzzy rules using Evolutionary Learning techniques, such as Genetic Algorithms and Learning Classifier Systems. We present ELF, a system able to evolve a population of fuzzy rules to obtain a suboptimal Fuzzy Logic Controller. ELF tackles some of the problems typi ..."
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Cited by 59 (8 self)
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We discuss the problem of learning fuzzy rules using Evolutionary Learning techniques, such as Genetic Algorithms and Learning Classifier Systems. We present ELF, a system able to evolve a population of fuzzy rules to obtain a suboptimal Fuzzy Logic Controller. ELF tackles some of the problems typical of the Evolutionary Learning approach: competition and cooperation between fuzzy rules, evolution of general fuzzy rules, imperfect reinforcement programs, fast evolution for realtime applications, dynamic evolution of the focus of the search. We also present some of the results obtained from the application of ELF to the development of Fuzzy Logic Controllers for autonomous agents and for the classical cartpole problem. INTRODUCTION Genetic Algorithms (GAs)[13] and Learning Classifier Systems (LCS)[7][8] emerged in the last years as powerful Evolutionary Learning (EL) techniques to identify systems that optimize some cost function. The cost function provides a reinforcement that gui...
Evolving fuzzy rule based controllers using genetic algorithms
 FUZZY SETS AND SYSTEMS
, 1996
"... The synthesis of geneticsbased machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multivariate nonlinear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presente ..."
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Cited by 49 (1 self)
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The synthesis of geneticsbased machine learning and fuzzy logic is beginning to show promise as a potent tool in solving complex control problems in multivariate nonlinear systems. In this paper an overview of current research applying the genetic algorithm to fuzzy rule based control is presented. A novel approach to geneticsbased machine learning of fuzzy controllers, called a Pittsburgh Fuzzy Classifier System # 1 (PFCS1) is proposed. PFCS1 is based on the Pittsburgh model of learning classifier systems and employs variable length rulesets and simultaneously evolves fuzzy set membership functions and relations. A new crossover operator which respects the functional linkage between fuzzy rules with overlapping input fuzzy set membership functions is introduced. Experimental results using PFCS1 are reported and compared with other published results. Application of PFCS1 to a distributed control problem (dynamic routing in computer networks) is also described and experimental results are presented.
A Multistrategy Learning Scheme For Agent Knowledge Acquisition
 Informatica
, 1993
"... this paper). Although room does not permit listing them all, some examples are: TF bearing(X,Y) = right AND turn(X) = left THEN heading(Y,X) headon ..."
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Cited by 38 (3 self)
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this paper). Although room does not permit listing them all, some examples are: TF bearing(X,Y) = right AND turn(X) = left THEN heading(Y,X) headon
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
 IEEE Transactions on Systems, Man and Cybernetics, Part B
"... Abstract—An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO ..."
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Cited by 35 (1 self)
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Abstract—An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upperhalf of the bestperforming individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi–Sugeno–Kangtype recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority. Index Terms—Dynamic plant control, elite strategy, recurrent neural/fuzzy work, temporal sequence production. I.