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49
Get Real! XCS with ContinuousValued Inputs
 LEARNING CLASSIFIER SYSTEMS, FROM FOUNDATIONS TO APPLICATIONS, LNAI1813
, 2000
"... Classifier systems have traditionally taken binary strings as inputs, yet in many real problems such as data inference, the inputs have real components. A modified XCS classifier system is described that learns a nonlinear realvector classification task. ..."
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Cited by 77 (2 self)
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Classifier systems have traditionally taken binary strings as inputs, yet in many real problems such as data inference, the inputs have real components. A modified XCS classifier system is described that learns a nonlinear realvector classification task.
MOGUL: A Methodology to Obtain Genetic fuzzy rulebased systems Under the iterative rule Learning approach
 International Journal of Intelligent Systems
, 1999
"... The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rulebased systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rulebased systems, i.e., evolutionary algorithmbased pro ..."
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Cited by 31 (20 self)
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The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rulebased systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rulebased systems, i.e., evolutionary algorithmbased processes to automatically design fuzzy rulebased systems by learning and�or tuning the fuzzy rule base, following the same generic structure and able to cope with problems of a different nature. A specific evolutionary learning process obtained from the paradigm proposed to design unconstrained approximate Mamdanitype fuzzy rulebased systems will be introduced, and its accuracy in the solving of a realworld electrical engineering problem will be analyzed. � 1999 John Wiley & Sons, Inc. 1.
Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rulebased systems
, 2001
"... ..."
Evolutionary Algorithms for Fuzzy Control System Design
, 2000
"... This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance ind ..."
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Cited by 21 (3 self)
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This paper provides an overview on evolutionary learning methods for the automated design and optimization of fuzzy logic controllers. In a genetic tuning process an evolutionary algorithm adjusts the membership functions or scaling factors of a predefined fuzzy controller based on a performance index that specifies the desired control behavior. Genetic learning processes are concerned with the automated design of the fuzzy rule base. Their objective is to generate a set of fuzzy ifthen rules that establishes the appropriate mapping from input states to control actions. We describe two applications of geneticfuzzy systems in detail, an evolution strategy that tunes the scaling and membership functions of a fuzzy cartpole balancing controller and a genetic algorithm that learns the fuzzy control rules for an obstacle avoidance behavior of a mobile robot.
A twostage evolutionary process for designing TSK fuzzy rulebased systems
 IEEE Trans. Syst., Man, Cybern. B
, 1999
"... Abstract—Nowadays, fuzzy rulebased systems are successfully applied to many different realworld problems. Unfortunately, relatively few wellstructured methodologies exist for designing them and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the ..."
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Cited by 19 (10 self)
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Abstract—Nowadays, fuzzy rulebased systems are successfully applied to many different realworld problems. Unfortunately, relatively few wellstructured methodologies exist for designing them and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi–Sugeno –Kang (TSK) fuzzy rulebased systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a twostage evolutionary process for designing TSK fuzzy rulebased systems from examples combining a generation stage based on a ( ;)evolution strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary knowledge base, and a refinement stage, in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process composed of a genetic algorithm and an evolution strategy to obtain the final Knowledge Base whose rules cooperate in the best possible way. Some aspects make this process different from others proposed until now: the design problem is addressed in two different stages, the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolutionary Algorithms that causes the search process to obtain good solutions more quickly. The performance of the method proposed is shown by solving two different problems: the fuzzy modeling of some threedimensional surfaces and the computing of the maintenance costs of electrical medium line in Spanish towns. Results obtained are compared with other kind of techniques, evolutionary learning processes to design TSK and Mamdanitype fuzzy rulebased systems in the first case, and classical regression and neural modeling in the second. Index Terms — Evolution strategies, evolutionary algorithms, genetic algorithms, learning, Takagi–Sugeno –Kang (TSK) fuzzy
Delayed Reinforcement, Fuzzy QLearning and Fuzzy Logic Controllers
 In
, 1996
"... . In this paper, we discuss situations arising with reinforcement learning algorithms, when the reinforcement is delayed. The decision to consider delayed reinforcement is typical in many applications, and we discuss some motivations for it. Then, we summarize QLearning, a popular algorithm to dea ..."
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Cited by 11 (5 self)
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. In this paper, we discuss situations arising with reinforcement learning algorithms, when the reinforcement is delayed. The decision to consider delayed reinforcement is typical in many applications, and we discuss some motivations for it. Then, we summarize QLearning, a popular algorithm to deal with delayed reinforcement, and its recent extensions to use it to learn fuzzy logic structures (Fuzzy QLearning). Moreover, we present how a reinforcement learning algorithm we have developed in the past (ELF  Evolutionary Learning of Fuzzy rules) implements an extension of the popular QLearning algorithm for the distribution of delayed reinforcement when the controller to be learnt is a Fuzzy Logic Controller (FLC). Finally, we present some examples of the application of ELF to learning FLCs that implement behaviors for an autonomous agent. Keywords. Fuzzy Logic Controllers, QLearning, Reinforcement Learning. 1. Introduction The main goal of the research presented in this paper i...
Learning to Compose Fuzzy Behaviors for Autonomous Agents
, 1997
"... In this paper, we present SELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. SELF learns context metarules that are used to coordinate basic behaviors in order to perform complex ta ..."
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Cited by 11 (0 self)
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In this paper, we present SELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. SELF learns context metarules that are used to coordinate basic behaviors in order to perform complex tasks in a partially and imprecisely known environment. Context metarules are expressed in terms of positive and negated fuzzy predicates. We also show how SELF can learn robust and portable behaviors, thus reducing the time and effort to design behaviorbased agents . 1. Introduction Since the first Brooks's seminal papers [11] [12], many autonomous agents have been implemented following the behaviorbased paradigm, where the Address correspondence to Andrea Bonarini Dipartimento di Elettronica e Informazione Politecnico di Milano Piazza Leonardo da Vinci, 32  20133 Milano  Italy Phone: +39 2 2399 3525  Fax: +39 2 2399 3411 Email: bonarini@elet.polimi.it We would like to thank A. ...
Evolutionary Design of Fuzzy Logic Controllers
 Mathware & Soft Computing
, 1996
"... An evolutionary approach to fuzzy logic controller design is presented in this paper. We propose the use of a class of genetic algorithms to produce suboptimal fuzzy rulebases (internally represented as constrained syntactic trees). This model has been applied to the cart centering problem. The obt ..."
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Cited by 11 (0 self)
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An evolutionary approach to fuzzy logic controller design is presented in this paper. We propose the use of a class of genetic algorithms to produce suboptimal fuzzy rulebases (internally represented as constrained syntactic trees). This model has been applied to the cart centering problem. The obtained results show that a good parameterization of the algorithm and an appropriate evaluation function lead to nearoptimal solutions.
Encouraging Cooperation in the Genetic Iterative Rule Learning Approach for Qualitative Modeling
, 1999
"... . 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 RuleBased Systems for Qualitative Modeling. These kinds of genetic processes, called Genetic Fuzzy RuleBased Systems, may be b ..."
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Cited by 10 (1 self)
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. 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 RuleBased Systems for Qualitative Modeling. These kinds of genetic processes, called Genetic Fuzzy RuleBased 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 RuleBased 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...