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   COLLOQUIUM OF THE LABORATORY FOR COMPUTER DESIGN OF MATERIALS
                   School of Computational Sciences
                           (CSI 898-Sec 001)
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Multi-Objective Genetic Algorithms: Introduction and Some Advances

                          Shapour Azam
               Department of Mechanical Engineering
             University of Maryland, College Park, MD

Multi-Objective Genetic Algorithms (MOGAs) are used to obtain a set
of Pareto optimum solutions for a multiobjective design optimization
problem.  Unlike conventional gradient-based multiobjective optimization
methods, MOGAs obtain a set of Pareto solutions with a single run of 
a Genetic Algorithm (GA).  One difference between a MOGA and a GA is 
how the solution points, as they evolve, are ranked.  The other is
how the constrains are handled.  One might also be interested to 
assess the 'goodness' of a set of Pareto solutions.

In this presentation, an introduction to multiobjective optimization 
and in particular MOGAs will be given.  We will present a MOGA that
was recently developed by our research group.  We will also discuss
methods for handling the constraints in a MOGA, and also quantifying
the quality of a set of Pareto solutions.  As a demonstration, we will
present an application of the MOGA to several engineering design 
examples.

                         Monday , April 15, 2002
                                  4:30 pm
                        Room 206, Science & Tech. I

Refreshments will be served at 4:15 PM.
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Find the schedule at http://www.csi.gmu.edu/lcdm/seminar/schedule.html