An Automatic Pareto Classifier for the Multiobjective Optimization of an Electrostimulative Acetabular Revision System

In this paper, we present a Pareto classifier for a multiobjective evolutionary algorithm used to optimize the numerical model of an electrostimulative acetabular revision system. This classifier is based on the hyper-radial visualization method and enables us to automatically choose the most efficient stimulation electrode arrangement from a Pareto amount of optimal solutions to treat the pelvic bone after a hip revision surgery. It is based on two criteria: 1) the average performance concerning all optimization goals and 2) the general strength of tradeoffs in each goal function to achieve this performance.