Genetic algorithms in structural optimum design using convex models of uncertainty

This chapter focuses on the use of convex models of uncertainty with genetic algorithms for optimal structural design. The chapter is comprised of five sections. Section 1 is a literature review of convex models and their application to optimal structural design and other engineering fields. Section 2 explores the use of convex models to deal with uncertainties as an alternative to the more traditional probabilistic approach. In this section the superposition method to implement the uniform bound convex model is illustrated. Section 3 underlines the benefits of incorporating genetic algorithms in optimal structural design. Section 4 presents applications of convex models to optimal structural design and Section 5 suggests new avenues for research and application of convex models. This chapter grows from research conducted since 2000 by the Gonzaga University Center for Evolutionary Algorithms. A preliminary version was published by Millpress as (Ganzerli et al. 2003).