A Genetic Algorithm as a Learning Method Based on Geometric Representations

2003 ◽  
pp. 1588-1589
Author(s):  
Gregory A. Holifield ◽  
Annie S. Wu
Keyword(s):  
Genetic Algorithm ◽  
Learning Method ◽  
Geometric Representations

Cluster Based Medical Image Registration Using Optimized Neural Network

2017 ◽  
pp. 1437-1467
Author(s):  
Joydev Hazra ◽  
Aditi Roy Chowdhury ◽  
Paramartha Dutta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Registration ◽  
Convergence Rate ◽  
Optimization Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Learning Method ◽  
Local Minima ◽  
The Neural Network

Registration of medical images like CT-MR, MR-MR etc. are challenging area for researchers. This chapter introduces a new cluster based registration technique with help of the supervised optimized neural network. Features are extracted from different cluster of an image obtained from clustering algorithms. To overcome the drawback regarding convergence rate of neural network, an optimized neural network is proposed in this chapter. The weights are optimized to increase the convergence rate as well as to avoid stuck in local minima. Different clustering algorithms are explored to minimize the clustering error of an image and extract features from suitable one. The supervised learning method applied to train the neural network. During this training process an optimization algorithm named Genetic Algorithm (GA) is used to update the weights of a neural network. To demonstrate the effectiveness of the proposed method, investigation is carried out on MR T1, T2 data sets. The proposed method shows convincing results in comparison with other existing techniques.

A Multi Autonomic Objects Flexible Workflow and its Knowledge Acquisition Supporting Engineering Design Process

2010 ◽  
Vol 97-101 ◽  
pp. 3341-3344
Author(s):  
Dong Bo Wang ◽  
Xiu Tian Yan ◽  
Ning Sheng Guo ◽  
Tao Li
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Literature Review ◽  
Knowledge Acquisition ◽  
Engineering Design ◽  
Design Process ◽  
Design Activity ◽  
Learning Method ◽  
Matlab Simulation ◽  
Engineering Design Process

In order to support the dynamic and creative Engineering Design Process (EDP) comprehensively, after a detailed literature review, a multi autonomic objects (AO) flexible workflow is applied into the supporting and management of EDP, its support for decision making, EDP evolution and design activity granularity is explained, finally and most importantly, a genetic algorithm-based AO knowledge learning method is proposed, the algorithm is demonstrated by a MATLAB simulation that it can satisfy the knowledge acquisition in EDP satisfactorily.

scholarly journals A Learning Method of Scheduling Knowledge by Genetic Algorithm

1996 ◽  
Vol 116 (5) ◽  
pp. 577-583
Author(s):  
Yoshitomo Ikkai ◽  
Masaaki Inoue ◽  
Takenao Ohkawa ◽  
Norihisa Komoda
Keyword(s):  
Genetic Algorithm ◽  
Learning Method
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