Comparison of methods for developing dynamic reduced models for design optimization

Authors:
K. Rasheed;X. Ni;S. Vattam
Affiliations:
The University of Georgia, Department of Computer Science, 30602, Athens, GA, USA;North Carolina, State University, Department of Statistics, 27695, Raleigh, NC, USA;Georgia Institute of Technology, College of Computing, 30332, Atlanta, GA, USA
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2005

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Abstract

In this paper we compare three methods for forming reduced models to speed up genetic-algorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed. Empirical results in several engineering design domains are presented.