Recently, there has been extensive work on the construction of fuzzy controllers for mobile robots by a genetic algorithm (GA); therefore, we can realize evolutionary optimization as a promising method for developing fuzzy controllers. However, much investigation on the evolutionary fuzzy controller remains because most of the previous works have not seriously attempted to analyze the fuzzy con...

This paper proposes a TSK-type neural fuzzy network (TNFN) with a group interaction-based evolutionary algorithm (GIEA) for constructing the cancer cell colonies diagnosis system (CCCDS). The proposed GIEA is designed on the basis of symbiotic evolution which each chromosome in the population represents only partial solution. The whole solution consists of several chromosomes. The GIEA is diffe...

In this study, a new method has been proposed for rule extraction required for a fuzzy classification system using Cellular Learning Automata Based on Evolutionary Computing (CLA-EC) model. CLA-EC model is an evolutionary algorithm which is a result of the combination of a cellular learning automata with the concepts mentioned in evolutionary computing. It has been shown a higher applicability ...

DNA algorithm and fuzzy evolutionary clustering techniques are used to classify damaged images and to reconstruct the original images. Experimental results show both methods are far more effective than the use of genetic algorithms or c-means clustering. Particularly, the method of fuzzy evolutionary clustering provides very fast convergence and accurate image reconstruction with absolute certa...

Contributors to the special track on Evolutionary Fuzzy Systems at the EUSFLAT 2003 conference were asked to record their thoughts and ideas on the current state of evolutionary fuzzy systems research, "burning issues" and future directions. This paper brings together these contributions.

This paper describes an application of evolutionary algorithms to the predictive modelling of customer behaviour in a business environment. Predictive models are represented as fuzzy rule bases, which allows for intuitive human interpretability of the results obtained, while providing satisfactory accuracy. An empirical case study is presented to show the effectiveness of the approach.

In this paper, an efficient approach of combining additive expression tree model (AET) with hybrid evolutionary method is proposed to identify nonlinear systems. As linear variant of additive tree model, additive expression tree model is proposed to encode the mathematical formulations. For finding the optimal structure and parameters of systems, a hybrid evolutionary method integrating a new s...

This work is related to the KEEL (Knowledge Extraction based on Evolutionary Learning) tool, an open source software that supports data management and provides a platform for the analysis of evolutionary learning for Data Mining problems of different kinds including as regression, classification, unsupervised learning. It includes a big collection of evolutionary learning algorithms based on di...

The effect of intelligent semi-active thermal exchange-fuzzy controller in structural rehabilitation by attenuating seismic responses of structural systems is investigated. In the suggested control system, MR dampers and sensors are employed as a semi-active controller. Resultant control forces of MR damper are administrated by providing external voltage supply, during the earthquakes and high ...

This research presents a novel design approach to achieve an optimal structure established upon multiple objective functions by simultaneous utilization of the Enhanced Time Evolutionary Optimization method and Fuzzy Logic (FLETEO). For this purpose, at first, modeling of the structure design problem in this space is performed using fuzzy logic concepts. Thus, a new problem creates with functio...