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The Effect of Regularization with Macroscopic Fitness in a Genetic Approach to Elastic Image Mapping
Kazuhiro MATSUI Yukio KOSUGI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1998/05/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence and Cognitive Science
genetic algorithm, regularization, elastic image mapping, consensus,
Full Text: PDF(658.1KB)>>
We introduce a concept of regularization into Genetic Algorithms (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 `smoothing 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.