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...

Evolutionary computation and evolutionary algorithms represent a developing science and technology that can be effectively applied to the generation and evaluation of system of systems architectures. A general technique used by systems engineering professionals is a binary matrix representation of a system or system of systems. The specific meaning and semantics of the binary relationship depen...

In essence, data mining consists of extracting knowledge from data. This paper proposes an evolutionary system for discovering fuzzy classi cation rules. Fuzzy logic is useful for data mining especially in the case for performing classi cation task. Three methods were used to extract fuzzy classi cation rules using Evolutionary Algorithms: (1) genetic selection small number of large number of f...

A new method for texture classification is proposed. It is composed of two processing stages, namely, a low level evolutionary feature extraction based on Gabor wavelets and a high level neural network based pattern recognition. This resembles the process involved in the human visual system. Gabor wavelets are exploited as the feature extractor. A neural network, Fuzzy Adaptive Resonance Theory...

This paper introduces an evolutionary optimization algorithm taking advantage of multiple populations and an adaptive aging parameter to achieve faster and more robust convergence. As challenging test cases, the evolutionary algorithm is used to optimize parameters for dynamical fuzzy systems. Our results show that the proposed algorithm is capable of outperforming the traditional reference alg...

in recent years, soft computing methods have generated a large research interest. the synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. a particular evolutionary algorithm (ea) is differential evolution (de). as for any ea, de algorithm also requires parameters tuning to achieve desirable performance. in this paper tuning the perturbation factor vector of de ...

It has been a practice in control system design to provide certain means of control in any control system by using different techniques such as adding compensators like PI, PD, and PID, with the incorporation of machine intelligence techniques such as fuzzy logic, artificial neural networks, and evolutionary computation. This paper presents a design approach in the development of compensators f...

Fuzzy systems are capable to model the inherent uncertainties in real world problems and implement human decision making. In this paper two issues related to fuzzy systems development are addressed and solutions are proposed and implemented. First issue is related to the high dimensional data sets. Such kinds of data sets lead to explode the search space of generated rules and results into dete...

This paper presents a new boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is built in an incremental fashion, in that the evolutionary algorithm extracts one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances...

Two ingredients of soft computing, evolutionary computing and fuzzy logic can be combined in a way that makes them beneet from one another. An evolutionary algorithm can evolve fuzzy systems, while fuzzy logic can be used to control evolution to speed up convergence to a global optimum and escape from local optima. Besides, concepts that are inherent in the workings of evolutionary algorithms, ...