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

Kazuhiro MATSUI; Yukio KOSUGI

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

Kazuhiro MATSUI, Yukio KOSUGI

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E81-D No.5 pp.472-478

Publication Date: 1998/05/25

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Category: Artificial Intelligence and Cognitive Science

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Kazuhiro MATSUI, Yukio KOSUGI, "The Effect of Regularization with Macroscopic Fitness in a Genetic Approach to Elastic Image Mapping" in IEICE TRANSACTIONS on Information, vol. E81-D, no. 5, pp. 472-478, May 1998, doi: .
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e81-d_5_472/_p

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@ARTICLE{e81-d_5_472,
author={Kazuhiro MATSUI, Yukio KOSUGI, },
journal={IEICE TRANSACTIONS on Information},
title={The Effect of Regularization with Macroscopic Fitness in a Genetic Approach to Elastic Image Mapping},
year={1998},
volume={E81-D},
number={5},
pages={472-478},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - The Effect of Regularization with Macroscopic Fitness in a Genetic Approach to Elastic Image Mapping
T2 - IEICE TRANSACTIONS on Information
SP - 472
EP - 478
AU - Kazuhiro MATSUI
AU - Yukio KOSUGI
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 1998
AB - 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.
ER -