The Effect of Regularization with Macroscopic Fitness in a Genetic Approach to Elastic Image Mapping

摘要

We introduce a concept of regularization into Genetic Algorithms (GAs). Conventional Gas include no ex- plicit regularizing operations. However, the regularization is very effective in solving ill-posed problems. So, we propose a method of regularization to apply Gas to ill-posed problems. This reg- ularization is a kind of consensus operation among neighboring individuals in Gas, and plays the role of 'smoothing the solu- tion.' Our method is based on the evaluation of macroscopic fitness, which is a new fitness criterion.