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IEEE DL: State-Of-The-Art Evolutionary Algorithms for Many Objective Optimization
Speaker(s): Prof. Gary Yen, Regents Professor in the School of Electrical and Computer Engineering, Oklahoma State University
Date: 10-11-2014
Time: 6:00 PM
Location: Algonquin College, 1385 Woodroffe Ave., School of Advanced Technology, Building-P, Room P215
Parking: No fee after 5:00 p.m. at Parking Lots 8 & 9. Please respect restricted areas.
Registration: Free to attend, but registration is required. To ensure a seat, please register by contacting Dr. Moufid Harb at mharb@ieee.org
Organizer(s): Dr. Moufid Harb
Organizer Email: mharb@ieee.org
Organized by: The IEEE Computational Intelligence Society / Systems, Man and Cybernetics Ottawa Joint Chapter and the IEEE Computer Society Ottawa Chapter
URL: http://www.ieeeottawa.ca/ci/nextMeetings.html
Abstract: Evolutionary computation is the study of biologically motivated computational paradigms which exert novel ideas and inspiration from natural evolution and adaptation. The applications of population-based heuristics in solving multiobjective optimization problems have been receiving a growing attention. To search for a family of Pareto optimal solutions based on nature-inspiring problem solving paradigms, Evolutionary Multiobjective Optimization Algorithms have been successfully exploited to solve optimization problems in which the fitness measures and even constraints are uncertain and changed over time.
When encounter optimization problems with many objectives, nearly all designs performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto optimality principle. This talk will survey recently published literature along this line of research- evolutionary algorithm for many-objective optimization and its real-world applications. Based on performance metrics ensemble, we will provide a comprehensive measure among all competitors and more importantly reveal insight pertaining to specific problem characteristics that the underlying evolutionary algorithm could perform the best. The experimental results confirm the finding from the No Free Lunch theorem: any algorithm’s elevated performance over one class of problems is exactly paid for in loss over another class.
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