Journal of Intelligent & Fuzzy Systems - Volume 6, issue 1

Authors: Cicalese, Ferdinando | Loia, Vincenzo

Article Type: Research Article

Abstract: Genetic algorithms are powerful and robust heuristic adaptation procedures suggested by biological evolution and molecular genetics. Fuzzy set theory and fuzzy logic have been proposed in order to provide some means for representing and manipulating imprecision and vagueness. In this paper genetic algorithms and fuzzy logic are combined in a uniform framework suitable for fuzzy classification. We discuss how a fuzzy classification methodology introduced in previous papers has been improved by becoming part of a genetic algorithm. The resulting genetic fuzzy classification technique shows increased sensitivity of solution, avoids the effect of fuzzy numbers grouping and allows for more effective …search over solution space. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 1, pp. 117-129, 1998

Authors: Ohsaki, Miho | Takagi, Hideyuki | Ohya, Kimiko

Article Type: Research Article

Abstract: This paper proposes a new input method for human operators of an interactive GA to reduce their psychological burden. This method utilizes discrete fitness values to reduce the psychological stress involved in the input procedure. The advantage and disadvantage of the proposed method are evaluated and discussed. We conducted simulations to investigate the influence of quantization noise stemming from the use of discrete fitness values on convergence. Results show that the quantization noise does not significantly worsen convergence in practical use. The proposed method and a variation of the method which combines both discrete and continuous fitness values are evaluated …using two subjective tests involving the task of drawing faces. The results of the subjective tests indicate that our proposed method and its variation can significantly reduce the psychological stress level of human interactive GA operators. The experimental results of the proposed method are discussed on its universality. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 1, pp. 131-145, 1998

Authors: Hlavacek, William S. | Savageau, Michael A.

Article Type: Research Article

Abstract: We illustrate a method of controlled mathematical comparison for determining design principles of biological control circuits. The method involves the comparison of alternative circuits that differ in a single process, that are constrained so as to eliminate extraneous differences, and that are compared quantitatively on the basis of a priori criteria for functional effectiveness. We use this method to compare two forms of coupling that are important in the regulation of repressible gene expression. The first form, which is called complete uncoupling, is marked by a constant level of regulator protein. The second form, which is called perfect coupling, is …marked by a level of regulator protein that varies coordinately with the level of repressible enzyme under control. Our results indicate that, for a large class of systems, performance is better with perfect coupling than with complete uncoupling if the regulator protein is a repressor, and conversely, that performance is better with complete uncoupling than with perfect coupling if the regulator protein is an activator. These results lead to testable predictions that are consistent with available data. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 1, pp. 147-160, 1998

Authors: Kang, Geuntaek | Lee, Wonchang | Kang, Yun-Seog | Jeong, Seong-Wook | Kim, Jae-In | Lee, Hong-Won

Article Type: Research Article

Abstract: The soft computing methods are used for the control of refrigerator temperature. An axis having blades is introduced in the refrigerator compartment to control the direction of cold air, and the directions of the blades are controlled by a neural networks controller so that more cold air is sent where the temperature is high in the refrigerator compartment. The neural networks controller is taught by a TSK fuzzy supervisor. The TSK fuzzy supervisor has the form of TSK fuzzy model. For the identification of fuzzy sets parameters in TSK fuzzy models, a new method made by combining the genetic algorithm …with the complex method is suggested. With the new method, more optimal parameters of fuzzy sets can be searched more speedily. Show more

Citation: Journal of Intelligent and Fuzzy Systems, vol. 6, no. 1, pp. 161-170, 1998