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Tutorial Details: Parallel Strategies With Genetic Algorithms

Date: Thursday, March 29, 2007, 01:00 pm - 03:00 pm
Location: 585 Walter
Instructor(s): Benjamin J. Lynch, MSI

Many scientific problems require the optimization of multidimensional functions that have dozens or even thousands of local minima and maxima. Local optimization methods such as the Newton-Raphson method and its many variants can find nearby local extrema but have little or no chance of finding a global minimum or maximum. Using a genetic algorithm (GA) is often a very efficient means to find a global minimum or maximum for complex functions.

This hands-on tutorial will show how genetic algorithms work and will discuss general optimization strategies with GAs. It will also work through a few GA problems using parallel GA optimization routines.

Level: Intermediate
Prerequisites: Basic skills in MPI and Fortran or C