The 2nd Deep Learning and Artificial Intelligence Winter School 2018
10th – 13th December 2018, Bangkok, Thailand

Gary G. Yen
Professor in the School of Electrical and Computer
Engineering, Oklahoma State University, USA

Topic: State-of-the-Art Evolutionary Algorithms for Many Objective Optimization andVisualization and Performance Metrics in Many-Objective Optimization
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 inwhich the fitness measures and even constraints are uncertain and changed over time.
When encounter optimization problems with many objectives, nearly all designs perform
poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto
optimality principle. The first talk will survey recently published literature along this line ofresearch- evolutionary algorithm for many-objective optimization and its real-world
applications. In addition to various Many-Objective Evolutionary Algorithms proposed in thelast few years, the second talk will be devoted to address three issues to complete the realworld applications at hand- visualization, performance metrics and multi-criteria decisionmaking for the many-objective optimization. Visualization of population in a highdimensional1 objective space throughout the evolution process presents an attractive featurethat could be well exploited in designing many-objective evolutionary algorithms. A
performance metric tailored specifically for many-objective optimization is also designed,
preventing various artifacts of existing performance metrics violating Pareto optimality
principle. A minimum Manhattan distance (MMD) approach to multiple criteria decision
making in many-objective optimization problems is proposed. This procedure is equivalent
to the knee selection described by a divide and conquer approach that involves iterations ofpairwise comparisons
Bio:

Gary G. Yen received the Ph.D. degree in electrical and computer engineering fromthe University of Notre Dame in 1992. He is currently a Regents Professor in the School ofElectrical and Computer Engineering, Oklahoma State University. His research interest
includes intelligent control, computational intelligence, evolutionary multiobjectiveoptimization, 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

The 2nd Deep Learning and Artificial Intelligence Winter School 2018
10th – 13th December 2018, Bangkok, Thailand

Technology, IEEE Transactions on Systems, Man and Cybernetics and IFAC Journal onAutomatica and Mechatronics during 2000-2010. He is currently serving as an associate
editor for the IEEE Transactions on Evolutionary Computation and IEEE Transactions onCybernetics. 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