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

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概要

• 論文の詳細を見る

• We introduce a concept of regularization into Genetic Algorithm(GAs). Conventional GAs include no explicit 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 regularization is a kind of consensus operation among neighboring individuals in GAs, and plays the role of 'smoothig the solution.' Our method is based on the evaluation of macroscopic fitness, which is a new fitness criterion. Conventional fitness of an individual in GAs is defined only from the phenotype of the individual, whereas the macroscopic fitness of an individual is evaluated from the phenotypes of the individual and its neighbors. We tested our regularizing operation by means of experiments with an elastic image mapping problem, and showed the effectiveness of the regularization.

• 社団法人電子情報通信学会の論文
• 1998-05-25

著者

• Kosugi Yukio

  The Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

• Matsui K

  First Department Of Pathology School Of Medicine Toyama Medical And Pharmaceutical University

• MATSUI Kazuhiro

  the Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

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