C. Karr, Genetic algorithms for fuzzy controllers, AI Expert, vol. 6, pp. 26-33, 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and genetic programming (GP) are distinguished by the genetic structures that undergo adaptation and the genetic operators bywhich they generate new variants. Recently, numerous publications propose evolutionary algorithms to automate the knowledge acquisition step in fuzzy system design [8] [9], 10] 11] These methods are described by the general term genetic fuzzy systems. Genetic fuzzy systems are applicable to control design problems in which the objective is to maximize some performance index of the closed loop process itself, as the evolutionary optimization is solely based on a ....

....behavior for a mobile robot. The fuzzy controller maps the input perceived by a set of sonar senors to a control action, namely the turn rate of the robot. The majority of applications in the domain of genetic fuzzy systems is concerned with the optimization of fuzzy logic controllers [12] 13] [9], 14] 11] Several authors proposed evolutionary algorithms for learning robotic behaviors implemented by fuzzy control rules [15] 16] 17] 18] The ELF (evolutionary learning of fuzzy rules) system employs a credit assignment mechanism similar to reinforcement learning techniques [15] ....

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C. Karr, \Genetic algorithms for fuzzy controllers.," AI Expert, vol. 6, no. 2, pp. 26-33, February 1991.

.... [200] for an evolution strategy example, and [97] 201] for genetic algorithm examples) Similarly, both the rule base and membership functions of fuzzy systems can be optimized by evolutionary algorithms, typically yielding improvements of the performance of the fuzzy system (e.g. 202] [203], 204] 205] 206] The interaction of computational intelligence techniques and hybridization with other methods such as expert systems and local optimization techniques certainly opens a new direction of research towards hybrid systems that exhibit problem solving capabilities approaching ....

C. L. Karr, "Genetic algorithms for fuzzy controllers," AI Expert, vol. 6, no. 2, pp. 27--33, 1991.

....to a variety of problems. Fuzzy logic controllers tuned by genetic algorithms. Some fundamentals of theory and description of individual components, as well as the early implementations of fuzzy logic controllers tuned by genetic algorithms can be found in a series of publications [57] 68] [69], 70] 71] 72] 61] 22] 73] 74] and [75] Fuzzy logic controllers learning by genetic algorithms. In [76] two different approaches to apply genetic algorithms to fuzzy logic controllers are described. The first approach uses the knowledge base as the individual of the genetic system, ....

Karr C. L., Genetic algorithms for fuzzy controllers. Al Expert 6, 27-33, 1991

....(fitness) measure. Fuzzy modeling can be considered as an optimization process where part or all of the parameters of a fuzzy system constitute the search space. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling first appeared more than a decade ago [79, 80]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

....in general decisions taken using crisp thresholds are dangerous in a number of real world applications, we have moved our attention to fuzzy rules, for their intrinsic interpolative behavior. The idea of using evolutionary algorithms to design fuzzy systems date from the beginning of the Nineties [5,12] and a fair body of work has been carried out throughout the past decade [6,7,4] The approach we followed is largely based on the development of a previous work on the evolution of fuzzy controllers [11] Based on that work, we define a classifier as a rule base, of up to 256 rules, each ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, March 1991.

....inviting tools for learning the rules to be used in a FRBS. GA are also potentially useful for learning fuzzy rules due to some special traits that differentiate them from conventional techniques. In fact, recently, the use of Genetic Algorithms (GA) have been proposed for learning rules (Karr [7,8]) showing that AG are an appropriate tool for this problems. This papers present methods to be applied to special types of rules or to specific problems. We present here a more general approach to this problem using GA. A very important advantage of our method is that the number of fuzzy rules ....

Karr, C., Genetic Algorithms for Fuzzy Controllers, AI EXPERT, 26-33, Feb. 1991.

....(fitness) measure. Fuzzy modeling can be considered as an optimization process where part or all of the parameters of a fuzzy system constitute the search space. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling had first appeared about a decade ago [15, 16]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquidlevel system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

....(fitness) measure. Fuzzy modeling can be considered as an optimization process where part or all of the parameters of a fuzzy system constitute the search space. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling had first appeared about a decade ago [8, 9]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever 6 Carlos Andres Pena Reyes and Moshe Sipper growing ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

....the fuzzy rule surface [20] 25] or deep structures [21] from the available data. Tuning process: It assumes the existing of a previous definition for both structures provided by learning processes or experts and adjusts them with slight modi cations to increase the system performance [14] [15]. Traditionally, the tuning process has been used to t the deep structure by exclusively changing membership function meanings [3] 6] 11] 14] 15] 23] Some proposals to develop this tuning with di erent mechanisms such as extended membership function expressions have also been proposed ....

.... both structures provided by learning processes or experts and adjusts them with slight modi cations to increase the system performance [14] 15] Traditionally, the tuning process has been used to t the deep structure by exclusively changing membership function meanings [3] 6] 11] 14] [15], 23] Some proposals to develop this tuning with di erent mechanisms such as extended membership function expressions have also been proposed [5] On the other hand, recent contributions perform a tuning of the surface structures adjusting the symbolic representations [5] 10] with linguistic ....

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C.L. Karr, Genetic algorithms for fuzzy controllers, AI Expert 6:2 (1991) 26-33.

....and genetic programming (GP) are distinguished by the genetic structures that undergo adaptation and the genetic operators by which they generate new variants. Recently, numerous publications propose evolutionary algorithms to automate the knowledge acquisition step in fuzzy system design [8] [9], 10] 11] These methods are described by the general term genetic fuzzy systems. Genetic fuzzy systems are applicable to control design problems in which the objective is to maximize some performance index of the closed loop process itself, as the evolutionary optimization is solely based on a ....

....behavior for a mobile robot. The fuzzy controller maps the input perceived by a set of sonar senors to a control action, namely the turn rate of the robot. The majority of applications in the domain of genetic fuzzy systems is concerned with the optimization of fuzzy logic controllers [12] 13] [9], 14] 11] Several authors proposed evolutionary algorithms for learning robotic behaviors implemented by fuzzy control rules [15] 16] 17] PROCEEDINGS OF THE IEEE, VOL. XX, NO. Y, MONTH 2000 2 [18] The ELF (evolutionary learning of fuzzy rules) system employs a credit assignment ....

[Article contains additional citation context not shown here]

C. Karr, \Genetic algorithms for fuzzy controllers.," AI Expert, vol. 6, no. 2, pp. 26-33, February 1991.

....complex search spaces. They have proven worthwhile on numerous diverse problems, able to find near optimal solutions given an adequate performance (fitness) measure. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling first appeared about a decade ago [17], 26] These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an evergrowing number of domains, branching into areas as ....

C. L. Karr, "Genetic algorithms for fuzzy controllers," AI Expert, vol. 6, no. 2, pp. 26--33, February 1991.

....(fitness) measure. Fuzzy modeling can be considered as an optimization process where part or all of the parameters of a fuzzy system constitute the search space. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling had first appeared about a decade ago [12,13]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

Karr CL. Genetic algorithms for fuzzy controllers. AI Expert 1991;6(2):26 -- 33.

....(fitness) measure. Fuzzy modeling can be considered as an optimization process where part or all of the parameters of a fuzzy system constitute the search space. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling had first appeared about a decade ago [10,11]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

....(fitness) measure. Fuzzy modeling can be considered as an optimization process where part or all of the parameters of a fuzzy system constitute the search space. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling had first appeared about a decade ago [18, 19]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

....complex, search spaces. They have proven worthwhile on numerous diverse problems, able to find nearoptimal solutions given an adequate performance (fitness) measure. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling first appeared about a decade ago [15, 16]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

....Figure 2 Basic structure of a fuzzy inference system. merous diverse problems, able to find near optimal solutions given an adequate performance (fitness) measure. Works investigating the application of evolutionary techniques in the domain of fuzzy modeling had first appeared about a decade ago [4, 5]. These focused mainly on the tuning of fuzzy inference systems involved in control tasks (e.g. cart pole balancing, liquid level system, and spacecraft rendezvous operation) Evolutionary fuzzy modeling has since been applied to an ever growing number of domains, branching into areas as diverse ....

C. L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991.

.... A taxonomy of this process can be made from two di erent angles: From the e ect generated in the membership function shapes point of view (i.e. how to tune such membership functions) the most usual ways are the following: Changing the basic parameters de ning the membership functions [5, 11, 19, 20, 21, 22, 29, 33]. One of the most common ways of tuning the membership functions is to change the basic parameters de ning such functions. For example, if the following triangular shape membership function is considered: x) 8 : x a b a ; if a x b c x c b ; if b x c ; 0; ....

....DB (e.g. 20, 21, 22, 33] A posteriori tuning: This approach improves the preliminary DB de nition considered once the RB have been derived. In this case, a tuning process considering the whole KB obtained by any method (the preliminary DB and the derived RB) is performed a posteriori (e.g. [5, 9, 11, 16, 19, 21, 29]) Of course, both approaches can be also considered jointly, rst developing a learning process with embedded tuning and subsequently performing an a posteriori tuning (e.g. 21] Finally, we should say that the tuning task gives more exibility to the learning process but it runs the risk of ....

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C.L. Karr, Genetic algorithms for fuzzy controllers, AI Expert 6:2 (1991) 26-33.

....inviting tools for learning the rules to be used in a FRBS. GA are also potentially useful for learning fuzzy rules due to some special traits that differentiate them from conventional techniques. In fact, recently, the use of Genetic Algorithms (GA) have been proposed for learning rules (Karr [7,8]) showing that AG are an appropriate tool for this problems. This papers present methods to be applied to special types of rules or to specific problems. We present here a more general approach to this problem using GA. A very important advantage of our method is that the number of fuzzy rules ....

Karr, C., Genetic Algorithms for Fuzzy Controllers, AI EXPERT, 26-33, Feb. 1991.

....ones. This is, because the optimized rule base seems to be very good, and because the cart pole problem is a relative artificial and easy problem. IV. Other Approaches In this section we shortly review other approaches and discuss the differences to ours. The Approach of C. Karr C. Karr [6, 5] uses genetic algorithms to alter just the shape of the fuzzy sets used in a given rule base. Each parameter of a fuzzy set (left , middle , and right point) is coded as a seven bit binary number. The parameters of all sets are concatenated to a bit string used for the genetic algorithm. The ....

C. Karr, Genetic Algorithms for Fuzzy Controllers. AI Expert 2/1991, 27--33.

....of the linguistic labels. Therefore, the semantic of the linguistic terms depends on the specific rule in which such terms appear. The RB obtained will have an approximate behaviour ( 14] and [18] Both approaches have been used in the genetic tuning of FRBSs for modelling and control. Karr [48] uses a GA to learn the parameters of the fuzzy sets with triangular membership functions related to each one of the linguistic terms of the DB. Herrera et al. 34] propose a genetic process to tune trapezoidal and triangular membership functions, coding with real parameters the complete ....

Karr, C. (1991), "Genetic algorithms for fuzzy controllers," AI Expert, Vol. 6, pp. 26-33.

....system obtained is optimized by adjusting the remaining free parameters. 1. 2 Evolutionary Search Concepts for Data based Fuzzy Modelling In the literature, we nd three main application areas of evolutionary algorithms in the eld of fuzzy modelling: optimization of membership functions (e.g. [15]) optimization (generation) of rules (e.g. 16] and simultaneous optimization of both (e.g. 17] In the case of rule base optimization (generation) most evolutionary algorithms use a xed rule base structure of complete rules (e.g. 17] where one individual in the evolving population ....

C.L. Karr. Genetic algorithms for fuzzy controllers. Al Expert, pages 393-404, 1991.

....list contains all items classified as books. ffl none 4.2 Journal articles The following list contains the references to every journal article included in this bibliography. The list is arranged in alphabetical order by the name of the journal. Acta Forestalia Fennica, 15] AI Expert, [45, 46] AISB Quarterly, 74] Analytica Chimica Acta, 25] Analytical Chemistry, 22] Chemometrics and Intelligent Laboratory Systems, 36, 27, 28] Computers Operations Research, 76] Engineering Applications of Artificial Intelligence, 35, 59] European Journal of Operational Research, 77] ....

....Hohfeld, Markus, 119] Hohn, Christian, 63, 64, 65, 66, 67] Huber, Reinhold, 94, 95] Hurme, Markku, 118] Hyotyniemi, Heikki, 93] Jaske, Harri, 92] Julstrom, Bryant A. 17, 18, 19, 20, 21] Jumppanen, Anne, 96] Kampen, Antoine H. C. van, 12] Karatza, Helen, 85] Karr, Charles L. [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62] Kateman, Gerrit, 22, 23, 24, 25, 26, 27, 28] Kettunen, Arto, 13, 14, 15, 16] King, e.g. 47, 49] Kohlmorgen, Udo, 112, 113, 115] Koivisto, Hannu, 91] Koivo, Heikki, 93] Koskimaki, Esa, 90] Kumar, K. K. 48] Kyngas, Jari, 96, 97] Kyyro, J. 96] Lahdelma, Risto, 9, 128] ....

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Charles L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, February 1991. ga:Karr91c.

....Evolutionary Computation, 429] total 2 books 4.2 Journal articles The following list contains the references to every journal article included in this bibliography. The list is arranged in alphabetical order by the name of the journal. Acta Electronica Sinica (China) 181] AI Expert, [686, 345, 702] Appl. Comput. Electromagn. Soc. J. USA) 252] Arch. Control Sci. Poland) 589] Archives of Control Sciences, 240] Artif. Intell. Eng. UK) 569, 187] Automatisierungstechnik (Germany) 609] Bad. Oper. Decyzje (Poland) 121] Biomed. Eng. Appl. Basis Commun. Taiwan) 594] Comput. ....

....Soon Won, 612] Kacprzyk, Janusz, 727, 508, 534, 271] Kacprzyk, J. 179] Kagan, N. 318] Kamei, K. 386, 572, 714] Kaminsky, A. R. 642] Kandel, Abraham, 32, 125, 765] Kanemitsu, H. 389] Karaboga, D. 485, 255, 353, 696] Karasawa, S. 477] Kariya, N. 509] Karr, Charles L. [489, 376, 730, 62, 63, 64, 65, 152, 156, 194, 272, 442, 343, 685, 344, 686, 345, 687, 688, 443, 689, 346, 690, 444, 347, 691, 348, 349, 445] Kasabov, N. K. 643] Kato, K. 641, 658] Kato, Kosuke, 707, 544, 608, 300, 308] Kawamura, Hiroshi, 583] Ke, J. Y. 638] Kelemen, Arpad, 87] Kernin, Jean Pierre, 170] Khalid, M. 205] Khedkar, Pratap S. 752] Khedkar, Protap S. 67] Kikuchi, Hiroaki, 649] Kikuchi, H. ....

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Charles L. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26-33, February 1991. ga:Karr91c.

....set of membership functions. This is a second point where GAs could be applied with a tuning purpose. As in the previous case of scaling functions, the main idea is the definition of parameterized functions and the subsequent adaptation of parameters. Some approaches are found to be in [4, 3, 24, 30, 48] The different proposals differ in the coding scheme and the management of the solutions (fitness functions, 4.2.1 Shape of the membership functions Two main groups of parameterized membership functions have been proposed and applied: piecewise linear functions and differentiable ....

....function into genetic information a binary code is used in [46, 47] and the coefficient itself in [34] 4.2. 2 Scope of the semantics The genetic tuning process of membership functions is based on two variants, depending on the fuzzy model nature, whether approximate ( 24] or descriptive ( 9, 30] The descriptive fuzzy model is essentially a qualitative expression of the system. A KB in which the fuzzy sets giving meaning (semantic) to the linguistic labels are uniformly defined for all rules included in the RB. It constitutes a descriptive approach since the linguistic labels take the ....

C.L. Karr. Genetic algorithms for fuzzy controllers, AI Expert (1991) 26-33.

....or approximative, either to code the fuzzy partition maintaining a linguistic description of the system, or to code the rule membership functions tuning the parameters of a label locally for every rule, thereby obtaining a fuzzy approximative model. Different approaches are presented in [32, 39, 2, 23]. Genetic derivation of the RB. All the methods belonging to this family are suppose the existence a collection of fuzzy set membership functions giving meaning to the labels, a DB, and learning a rule base. Some approaches are presented in [33, 40, 36, 18, 19] Genetic learning of the KB. There ....

Karr, C., Genetic Algorithms for Fuzzy Controllers. AI Expert 6 (1991) 26-33.

....of the DB. The tuning of the fuzzy rule membership functions is an important task in the design of fuzzy systems. The tuning method using GAs fits the membership functions of the fuzzy rules dealing with their parameters according to a fitness function. Different approaches are presented in [3, 14, 19, 27]. Genetic derivation of the RB. All the methods belonging to this family are suppose the existence a collection of fuzzy sets giving meaning to the labels, a DB. These methods present learning approaches of the RB, using different representations, list of rules, decision tables, etc. Some ....

Karr, C., Genetic Algorithms for Fuzzy Controllers. AI Expert 6 (1991) 26-33.

....and design of fuzzy controllers by GAs. For brevity s sake we will limit this section to a few contributions. These methods differ mostly in the order or the selection of the various FC components that are tuned (termsets, rules, scaling factors) C. Karr, one of the precursors in this quest [see Karr (1991b) Karr (1991a) Karr (1993) used GAs to modify the membership functions in the termsets of the variables used by the FCs. Karr used a binary encoding to represent three parameters defining a membership value in each termset. The binary chromosome was the concatenation of all termsets. The ....

....fuzzy controllers by GAs. For brevity s sake we will limit this section to a few contributions. These methods differ mostly in the order or the selection of the various FC components that are tuned (termsets, rules, scaling factors) C. Karr, one of the precursors in this quest [see Karr (1991b) Karr (1991a) Karr (1993) used GAs to modify the membership functions in the termsets of the variables used by the FCs. Karr used a binary encoding to represent three parameters defining a membership value in each termset. The binary chromosome was the concatenation of all termsets. The fitness function ....

Karr, C.L. (1991). Genetic algorithms for fuzzy controllers. AI Expert, 6(2):27--33.

....between these two approaches depends on the existence of a previously primary DB definition. While learning processes do not need this previous definition, tuning processes works over it obtaining a more accurated one. Several methods have proposed in order to define the FLC DB using GAs [BN95, BMU95, FTH94, HLV95b, HTS93, Kar91b]. All of them are based on the existence of a previously defined RB, usually extracted from the process operator. Each chromosome involved in the evolution process will represent different DB definitions, that is, each one of the chromosomes will contain a coding of the whole membership functions ....

....either all fuzzy control rules using the same meaning for the system variables linguistic terms or a different approximation in what each rule presents its own meaning for the labels involved by it. In this subsection we analyze an example of each one of both groups. The method proposed by Karr [Kar91b], belonging to the first one, and the method of Herrera et al. HLV95b] belonging to the second one. The DB definition method proposed by Karr The approach of Karr [Kar91b] is based on the existence of primary fuzzy partitions of the different system variables input and output spaces. The GA is ....

[Article contains additional citation context not shown here]

Karr C. (February 1991) Genetic algorithms for fuzzy controllers. AI Expert pages 26--33.

.... need to assume that the search space is differentiable or continuous and can also iterate several times on each datum received [12, 32] In this context, the GA optimizer for a fuzzy logic controller (FLC) affords more reliability in global optimization than does the Adaptive Neural Net approach [5, 7, 20]. In this experiment, we study two types of GA noise assignment on the design of an optimal FLC. The first type is to assign noise to the search parameters as in the previous experiment. The second type is to introduce noise into the training data set. A brief review of the Fuzzy Logic Controller ....

C. Karr, "Genetic Algorithms for Fuzzy Controllers", AI Expert, vol.6, no.2, February, p26-33, 1991.

....parameters of the membership functions, minimizing a square error function defined by means of an input output data set for evaluation. Recent works have been centred on the the use of GAs altering the set definitions so that the FLC matches a suitable set of reference data as closely as possible [Kar91a, Kor93, Hes93, Var93, Her95a]. A chromosome represents one possible solution to the problem, that is, one possible FCR base . The fitness function itself depends on the task of the FLC, usually the square error can be considered, then the chromosome is tested by evaluating the training data set. 4.2 Learning controller ....

Karr, C., Genetic Algorithms for Fuzzy Controllers. AI Expert, February 1991, 26-33.

....inviting tools for learning the rules to be used in a FRBS. GA are also potentially useful for learning fuzzy rules due to some special traits that differentiate them from conventional techniques. In fact, recently, the use of Genetic Algorithms (GA) have been proposed for learning rules (Karr [7,8]) showing that AG are an appropriate tool for this problems. This papers present methods to be applied to special types of rules or to specific problems. We present here a more general approach to this problem using GA. A very important advantage of our method is that the number of fuzzy rules is ....

Karr, C., Genetic Algorithms for Fuzzy Controllers, AI EXPERT, 26-33, Feb. 1991.

.... Fuzzy Systems There are different aspects on the design of FS and FLCS, as for instance, how to get the set of fuzzy control rules, to decide the number of fuzzy rules, to decide the shape of the membership functions, to tune the fuzzy rules base, etc, in which GA have been successfully applied [Bon93, Cas93, Gey92, 93, Gon93, Her93a, c, Kar91a, b ,c, Kro93, Lee93a, Nom92, Sur93, Tak93, Thr91, Val91a, b]. As it is known, the problem of managing a fuzzy rules base (adquisition, learning, tuning) is of utmost importance in the development of fuzzy systems. In a general learning process we can distinguish different components: 1. A generation method of desirable fuzzy rules able to include the ....

Karr, C., Genetic Algorithms for Fuzzy Controllers. AI Expert, February 1991, 26-33.

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C. Karr, Genetic algorithms for fuzzy controllers, AI Expert, vol. 6, pp. 26-33, 1991.

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C. Karr. Genetic algorithms for fuzzy controllers. AI Expert, 6(2):26--33, 1991.

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C. Karr, "Genetic algorithms for fuzzy controllers.," AI Expert, vol. 6, no. 2, pp. 26--33, February 1991.

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C. Karr, Genetic algorithms for fuzzy controllers, AI Expert 6 (2) (1991) 26--33.

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:27--33, 1991.

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C. Karr, "Genetic Algorithms for fuzzy controllers", AI Expert, 2, 1991, pp. 27-33.

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Chuck Karr. "Genetic Algorithms for Fuzzy Controllers". In AI Expert, pages 27-33, Feb. 1991.

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