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 non-linear real-vector classification task.

Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. This contribution focus on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different Genetic Fuzzy Rule-Based Systems, i. e., evolutionary algorithm-based processes to automatically design Fuzzy Rule-Based Systems by learning and/or tuning the Fuzzy Rule Base, following the same generic structure and able to cope with problems of different nature. A specific evolutionary learning process obtained from the paradigm proposed to design unconstrained approximate Mamdani-type Fuzzy RuleBased Systems will be introduced, and its accuracy in the solving of a real-world Electrical Engineering problem will be analyzed.

Genetic Algorithms and Evolution Strategies are combined in order to build a multistage Hybrid Evolutionary Algorithm for learning constrained Approximate Mamdani-type Knowledge Bases from examples. The Genetic Algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed Fuzzy Rule-Based Systems. The proposed Genetic Fuzzy Rule-Based System is used to solve an Electrical Engineering problem and the results obtained are compared with other methods presenting different characteristics.

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 if-then rules that establishes the appropriate mapping from input states to control actions. We describe two applications of genetic-fuzzy systems in detail, an evolution strategy that tunes the scaling and membership functions of a fuzzy cart-pole balancing controller and a genetic algorithm that learns the fuzzy control rules for an obstacle avoidance behavior of a mobile robot.

Nowadays, Fuzzy Rule-Based Systems are successfully applied to many different real-world problems. Unfortunatelly, relatively few well-structured 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. TSK Fuzzy Rule-Based Systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK Fuzzy Rule-Based 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 ...

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 rule-bases (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 near-optimal solutions.

. 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 Q-Learning, a popular algorithm to deal with delayed reinforcement, and its recent extensions to use it to learn fuzzy logic structures (Fuzzy Q-Learning). 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 Q-Learning 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, Q-Learning, Reinforcement Learning. 1. Introduction The main goal of the research presented in this paper i...

. 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 Rule-Based Systems for Qualitative Modeling. These kinds of genetic processes, called Genetic Fuzzy Rule-Based 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 Rule-Based 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...

In this paper, we present S-ELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. S-ELF 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 S-ELF can learn robust and portable behaviors, thus reducing the time and effort to design behavior-based agents . 1. Introduction Since the first Brooks's seminal papers [11] [12], many autonomous agents have been implemented following the behavior-based 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 E-mail: bonarini@elet.polimi.it We would like to thank A. ...