Event Title
Fuzzy Algorithms: Applying Fuzzy Logic to the Golden Ratio Search to Find Solutions Faster
Location
Science Center Auditorium, University of Dayton
Start Date
22-4-2016 5:10 PM
Description
Applying the concept of fuzzy logic (an abstract version of Boolean logic) to well-known algorithms generates an abstract version (i.e., fuzzy algorithm) that often results in computational improvements. Precision may be reduced but counteracted by gaining computational efficiency. The trade-offs (e.g., small increase in space, loss of precision) for a variety of applications are deemed acceptable. The fuzzification of an algorithm can be accomplished using a simple three-step framework. Creating a new fuzzy algorithm goes beyond simply converting the data from raw data into fuzzy data by additionally converting the operators and concepts into their abstract equivalents. This paper demonstrates: (1) how to apply the general framework by developing a fuzzy algorithm for a simple linear search algorithm and (2) the success of this process through the development of the Fuzzy Golden Ratio Section Search.
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, Other Computer Sciences Commons, Theory and Algorithms Commons
Fuzzy Algorithms: Applying Fuzzy Logic to the Golden Ratio Search to Find Solutions Faster
Science Center Auditorium, University of Dayton
Applying the concept of fuzzy logic (an abstract version of Boolean logic) to well-known algorithms generates an abstract version (i.e., fuzzy algorithm) that often results in computational improvements. Precision may be reduced but counteracted by gaining computational efficiency. The trade-offs (e.g., small increase in space, loss of precision) for a variety of applications are deemed acceptable. The fuzzification of an algorithm can be accomplished using a simple three-step framework. Creating a new fuzzy algorithm goes beyond simply converting the data from raw data into fuzzy data by additionally converting the operators and concepts into their abstract equivalents. This paper demonstrates: (1) how to apply the general framework by developing a fuzzy algorithm for a simple linear search algorithm and (2) the success of this process through the development of the Fuzzy Golden Ratio Section Search.
Comments
Copyright © 2016 by the author. This paper was presented at the 2016 Modern Artificial Intelligence and Cognitive Science Conference, held at the University of Dayton April 22-23, 2016.