Using self organizing maps and genetic algorithms for model selection in multilevel optimization

In Multilevel Optimization there is usually a choice to be made between different models when carrying out design evaluations. The choice is between accurate / computationally expensive evaluations and approximate/computational cheap ones. Here, a strategy is sought for selecting between different models during the search. The focus of the paper is on preliminary work carried out using a self organizing map (SOM) for model selection.