JSM 2004 - Toronto

Abstract #300628

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Activity Number: 211
Type: Topic Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Quality and Productivity
Abstract - #300628
Title: Using a Genetic Algorithm to Generate Mixture Designs
Author(s): John J. Borkowski*+
Companies: Montana State University
Address: Dept. of Mathematical Sciences, Bozeman, MT, 59717,
Keywords: genetic algorithm ; maximin criterion ; D-optimality ; response surface design
Abstract:

A genetic algorithm (GA) is an evolutionary search strategy based on simplified rules of biological population genetics and theories of evolution. A GA maintains a population of candidate solutions for a problem, and then selects those candidates most fit to solve the problem. After the selection process, the most fit candidate solutions are combined and/or altered by reproduction operators to produce new solutions for the next generation. The process continues, with each generation evolving more fit solutions until an acceptable solution is evolved. A GA is developed to generate p-component mixture designs allowing for lower and upper bounds for each mixture component. Several criteria, including D-optimality and the maximin criteria, will be used as the fitness criterion in the GA.

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Revised March 2004