Classifier-assisted constrained evolutionary optimization for automated geometry selection of orthodontic retraction spring

In orthodontics, retraction springs made of metallic wires are often used to move a tooth with respect to another by the virtue of the spring back effect. Specially selected form of spring may result in accurate force and moment required to move the tooth towards direction that suits a particular patient. In current practice, the geometry remains to be selected manually by orthodontists and no substantial automation of such process has been proposed to date. In this paper, we experiment with the automated geometry selection of the orthodontic retraction spring using constrained evolutionary optimization. Particularly, a Classifier-assisted Constrained Memetic Algorithm (CCMA) is designed for the purpose. The main feature of CCMA lies in the ability to identify appropriate spring structures that should undergo further refinement using a classifier system to perform the inference. Comparison to the baseline canonical Genetic Algorithm (GA) and Memetic Algorithm (MA) further highlights the efficacy of the proposed approach. In addition, to also assert the robustness of the CCMA for general complex design, further studies on commonly used constrained benchmark problems and existing constrained evolutionary optimization methods are also reported in the paper.