State-Of-The-Art Many-Objective Evolutionary Algorithms For Optimization

Speaker:        Gary G. Yen, Regents Professor
                Oklahoma State University

Title:          "State-Of-The-Art Many-Objective Evolutionary Algorithms
                 For Optimization"

Date:           Monday, 26 September 2016

Time:           4:00pm - 5:00pm

Venue:          Lecture Theater F (near lifts 25/26), HKUST

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. In particular, focus will be placed on the design of
selection strategy, including mating selection and environmental
selection.  We will show the design of a coordinated selection strategy to
improve the performance of evolutionary algorithms in many-objective
optimization.  This selection strategy considers three crucial factors: 1)
the new mating selection criterion considers both the quality of each
selected parent and the effectiveness of the combination of selected
parents; 2) the new environmental selection criterion directly focuses on
the performance of the whole population rather than single individual
alone, and 3) both selection strategies are complement to each other and
the coordination between them in the evolutionary process can achieve a
better performance than each of them used individually.  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.


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Biography:

Gary G. Yen received the Ph.D. degree in electrical and computer
engineering from the University of Notre Dame in 1992.  He is currently a
Regents Professor in the School of Electrical and Computer Engineering,
Oklahoma State University.  His research interest includes intelligent
control, computational intelligence, evolutionary multiobjective
optimization, conditional health monitoring, signal processing and their
industrial/defense applications.

Gary was an associate editor of the IEEE Transactions on Neural Networks
and IEEE Control Systems Magazine during 1994-1999, and of the IEEE
Transactions on Control Systems Technology, IEEE Transactions on Systems,
Man and Cybernetics and IFAC Journal on Automatica and Mechatronics during
2000-2010.  He is currently serving as an associate editor for the IEEE
Transactions on Evolutionary Computation and IEEE Transactions on
Cybernetics.  Gary served as Vice President for the Technical Activities,
IEEE Computational Intelligence Society in 2004-2005 and is the founding
editor-in-chief of the IEEE Computational Intelligence Magazine,
2006-2009.  He was the President of the IEEE Computational Intelligence
Society in 2010-2011 and is elected as a Distinguished Lecturer for the
term 2012-2014.  He received Regents Distinguished Research Award from OSU
in 2009, 2011 Andrew P Sage Best Transactions Paper award from IEEE
Systems, Man and Cybernetics Society, 2013 Meritorious Service award from
IEEE Computational Intelligence Society and 2014 Lockheed Martin
Aeronautics Excellence Teaching award.  Currently he serves as the chair
of IEEE/CIS Fellow Committee and General Co-Chair of 2016 IEEE World
Congress on Computational Intelligence to be held in Vancouver, Canada.
He is a Fellow of IEEE and IET.