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...

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 Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

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

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...

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

Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots. 1 INTRODUCTION This is a progress report on our efforts to design intelligent robots for complex environments. The sort of applications we have in mind include sentry robots, autonomous delivery vehicles, undersea surveillance vehicles, and automated warehouse robots. In particular, we are investigating issues relating to machine learning, using multiple mobile robots to perform tasks such as playing hide-and-seek, tag, or competing to find hidden objects. Given the wide range of tasks in the area of robotics and learning, it may...

Abshct- Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership functions and fuzzy rule base, which is traditionally achieved by a tedious trial-and error process. This paper develops genetic algorithms for automatic design of high performance fuzzy logic controllers using sophisticated membership functions that intrinsically reflect the nonlinearities encounter in many engineering control applications. The controller design space is coded in base7 strings (chromosomes), where each bit (gene) matches the 7 discrete fuzzy value. The developed approach is subsequently applied to design of a proportional plus integral type fuzzy controller for a nonlinear water level control system. The performance of this control system is demonstrated higher than that of a conventional PID controller. For further comparison, a fuzzy proportional plus derivative controller is also developed using this approach, the response of which is shown to present no steady-state error. I.

Evolutionary algorithms are direct, global optimization algorithms gleaned from the model of organic evolution. The most important representatives, genetic algorithms and evolution strategies, are briefly introduced and compared in this paper, and their major differences are clarified. Furthermore, the paper summarizes the application possibilities of evolutionary algorithms in the design of fuzzy logic controllers. The optimization of fuzzy membership functions turns out to be a promising and successful application domain for evolutionary algorithms, while the automatic learning of fuzzy control rules by means of fuzzy classifier systems is still in an early stage of research.

Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively

Fuzzy logic and evolutionary computation have proven to be convenient tools for handling realworld uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms for the purpose of developing intelligent control systems. The potential of the genetic programming paradigm (GP) for learning rules for use in fuzzy logic controllers (FLCs) is evaluated by focussing on the problem of discovering a controller for mobile robot path tracking. Performance results of incomplete rule-bases compare favorably to those of a complete FLC designed by the usual trial-and-error approach. A constrained syntactic representation supported by structurepreserving genetic operators is also introduced. Keywords: fuzzy control, genetic programming, syntactic contraints, mobile robots, rule-base discovery. 1 Introduction Recent research and applications employing non-analytical methods of soft computing such as fuzzy logic and evolutionary computati...