Inverse problems and genetic algorithms

Inverse problems are omnipresent in natural and engineering sciences, for example, in material characterization. Impressive results have been obtained by applying analytical–numerical techniques to their solution; however, in many practical cases these methods present drawbacks, which impede their application. In this scenario, Genetic Algorithms (GAs) arise as interesting alternatives, especially for the solution of complicated inverse problems, such as those resulting from the modeling and characterization of complex nonlinear systems, such as in particular materials with nonlinear elastic behavior. In this chapter, we present a brief introduction to inverse problem solution, highlighting the difficulties inherent in the application of traditional analytical–numerical techniques, and illustrating how genetic algorithms may in part obviate these problems