For real–valued continuous optimization problems various evolutionary
algorithms (EAs) have obtained promising results on benchmark problems.
The Iterated Density–Estimation Evolutionary Algorithm (IDEA) is an example
of one such algorithm. However, little is known about the practical benefits of these algorithms even though AI–techniques are often favored in practice because of their general applicability and good performance on complicated real–world problems. In this paper we focus on one specific practical medical application that imposes many optimization tasks. The application is the simulation of minimally invasive vascular interventions. We compare the use of a hybrid IDEA with the conjugate gradients algorithm and a problem–specific optimization algorithm and indicate that although the application of the conjugate gradients algorithm already leads to highly useful results, IDEAs yet improve on these results in the area of scalability, making a clear statement that IDEAs can indeed also be useful in practice.
