There are two possible ways for integrating Fuzzy Logic and Genetic Algorithms. One involves the application of Genetic Algorithms for solving optimization and search problems related with fuzzy systems. The another, the use of fuzzy tools and Fuzzy Logic-based techniques for modeling different Genetic Algorithm components and adapting Genetic Algorithm control parameters, with the goal of improving performance. The Genetic Algorithms resulting from this integration are called Fuzzy Genetic Algorithms. In this contribution, we tackle Fuzzy Genetic Algorithms by analyzing their definition based on the Zadeh's concept of Fuzzy Algorithms and the two different meanings as Fuzzy Logic may be viewed. We review different approaches, attempt to identify some open issues and summarize a few new promising research directions on the topic. Keywords: Fuzzy Logic, Genetic Algorithms, Fuzzy Algorithms, Fuzzy Genetic Algorithms. This research has been supported by CICYT TIC96-0778. 1 Introductio...

Abstract: Evolutionary algorithms become very popular due to their searching skill in a solution space. The problem arises when we try to adjust used genetic operators and parameters. In literature one can find various, often sophisticated new genetic operators, specific for the particular task. In the proposed method a user can use a number of cooperating and specialized genetic algorithms with simple genetic operators and presumed parameters, but an intelligent agent takes care of tuning the parameters. The agent tunes parameters dynamically on the basis of observed results. We have defined a number of measures used by the agent as inputs for a fuzzy control system. The set of fuzzy rules can be defined using experts ’ knowledge. The main advantage of the proposed system is releasing of GAs users from onerous duty – the determination of genetic operators and values of their parameters. It is usually a time consuming task requiring extensive experience. The proposed system is flexible enough to solve the problems which potential solution can be represented as a string of real values. The paper presents the initial studies as well as the final proposition: GAAgent, an ensemble of cooperating genetic algorithms controlled by a set of fuzzy rules. The exemplary results are presented and discussed. I.