Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP), by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.

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

Nowadays Fuzzy Logic Controllers have been succesfully applied to a wide range of engineering control processes. Several tasks have to be performed in order to design an intelligent control system of this kind for a concrete application. One of the most important and difficult ones is the extraction of the expert known knowledge of the controlled system. The aim of this paper is to present an evolutionary process based on genetic algorithms and evolution strategies for learning the Fuzzy Logic Controller Knowledge Base from examples in three different stages. The process allows us to generate two different kinds of Knowledge Bases, descriptive and approximative ones, depending on the scope of the fuzzy sets giving meaning to the fuzzy control rule linguistic terms, taking preliminary linguistic variable

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 sub-optimal 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 real-time 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 cart-pole 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...

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.

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 17:36---PAGE PROOFS for John Wiley & Sons Ltd (jwbook.sty v3.0, 12-1-1995) A GENERAL STUDY ON GENETIC FUZZY SYSTEMS 3

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.

The aim of this paper is to present an evolutionary process based on genetic algorithms and evolution strategies for learning the Fuzzy Logic Controller Knowledge Base from examples. The performance of the method proposed is shown by measuring the accuracy of the Fuzzy Logic Controllers designed in the modeling of two three-dimensional control surfaces derived from two mathematical functions presenting different characteristics. The results obtained by a method based on the Wang and Mendel's Knowledge Base generation process are also shown, allowing to compare both processes.

Fuzzy rules cooperate in a Fuzzy Logic Controller (FLC) to produce the best action for a given situation. If we have a population of fuzzy rules controlling a device, and we would like to evolve the population to obtain optimal performance by Reinforcement Learning, rules should compete each other, since we would like to judge their proposals. Therefore, in this approach, cooperation and competition are two opposite, desired activities done by the population members. This may be a problem, if we consider that the evaluation function may be biased, as it may happen, for instance, when we are designing a controlled device such as an Autonomous Agent. The problem becomes even harder if we would like to learn general rules, i.e., rules containing don't care symbols in their antecedents, thus competing with many groups of other rules, in many different situations. In the paper we discuss these issues, and we present our solution, implemented in ELF (Evolutionary Learning of Fuzzy rules). We...