scholarly journals New method of discriminant analysis by utilizing genetic algorithm.

1999 ◽  
Vol 1 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Mitsuru OSAWA
Keyword(s):  
Genetic Algorithm ◽  
Discriminant Analysis ◽  
New Method

Collagen remodeling in the extracellular matrix of the cardiomyopathic Syrian hamster heart as assessed by FTIR attenuated total reflectance spectroscopy

1999 ◽  
Vol 77 (11) ◽  
pp. 1843-1855 ◽  
Author(s):  
Pamela S Bromberg ◽  
Kathleen M Gough ◽  
Ian MC Dixon
Keyword(s):  
Genetic Algorithm ◽  
Extracellular Matrix ◽  
Discriminant Analysis ◽  
Left Ventricle ◽  
Linear Discriminant Analysis ◽  
Collagen Content ◽  
Diffuse Fibrosis ◽  
Collagen Deposition ◽  
Linear Discriminant ◽  
Collagen Remodeling

Collagen type I and III deposition in the cardiac extracellular matrix contributes significantly to myocardial dysfunction. Diffuse and focal fibrosis is believed to accompany human congestive cardiomyopathy (CCM) associated with congestive heart failure (CHF). The left ventricle collagen remodeling that occurs in the hamster model of CCM is marked by left ventricle fibrosis, hypertrophy and dilation, proceeded by CHF post 150 days of age. The objectives of our study were to (i) evaluate changes in collagen deposition in the right (RV) and left (LV) ventricular tissue of cardiomyopathic (CMP) and control (CON) myocardium using FTIR ATR spectroscopy, (ii) classify the normal and diseased heart tissue using linear discriminant analysis (LDA) aided by a genetic algorithm (GA) selection of spectroscopically diagnostic regions in the mid-IR region, (iii) rationalize the spectroscopic differences between left/right ventricle tissue as well as CON/CMP tissue according to the pathophysiology documented for the UM-X7.1 strain of CMP hamsters. The presence of collagen in the tissue was confirmed spectroscopically by observation of the collagen IR fingerprint in the 1000-1800 cm-1 region. Difference spectroscopy was utilized to substantiate which tissue under comparison exhibited pronounced collagen content. Multivariate analysis (LDA) was carried out on user-selected spectral subregions and compared to class separation based on spectral subregions chosen nonsubjectively by a GA. Our study confirmed that the animals experienced LV collagen remodeling denoted by focal rather than diffuse fibrosis. In addition, RV collagen remodeling, denoted by decreased RV collagen content, appeared to accompany the increased LV collagen deposition found for the CMP animals.Key words: FTIR spectroscopy, collagen, cardiomyopathy, genetic algorithm, linear discriminant analysis.

scholarly journals Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

Incremental Hyperplane Partitioning for Classification

2013 ◽  
Vol 4 (2) ◽  
pp. 67-79 ◽  
Author(s):  
Tao Yang ◽  
Sheng-Uei Guan ◽  
Jinghao Song ◽  
Binge Zheng ◽  
Mengying Cao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Equations ◽  
Curse Of Dimensionality ◽  
Experimental Results ◽  
New Method ◽  
Complex Pattern ◽  
Classification Problems ◽  
Encoding Scheme ◽  
Incremental Approach ◽  
Higher Dimensional

The authors propose an incremental hyperplane partitioning approach to classification. Hyperplanes that are close to the classification boundaries of a given problem are searched using an incremental approach based upon Genetic Algorithm (GA). A new method - Incremental Linear Encoding based Genetic Algorithm (ILEGA) is proposed to tackle the difficulty of classification problems caused by the complex pattern relationship and curse of dimensionality. The authors solve classification problems through a simple and flexible chromosome encoding scheme, where the partitioning rules are encoded by linear equations rather than If-Then rules. Moreover, an incremental approach combined with output portioning and pattern reduction is applied to cope with the curse of dimensionality. The algorithm is tested with six datasets. The experimental results show that ILEGA outperform in both lower- and higher-dimensional problems compared with the original GA.

Optimizing Fourier Series Based Gaits for Modular Robots Using an Evolutionary Algorithm

2013 ◽  
Author(s):  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Genetic Algorithm ◽  
Fourier Series ◽  
Evolutionary Algorithm ◽  
Periodic Motion ◽  
Degrees Of Freedom ◽  
Series Representation ◽  
Robotic System ◽  
Experimental Results ◽  
New Method ◽  
Modular Robots

A new method of controlling and optimizing robotic gaits for a modular robotic system is presented in this paper. A robotic gait is implemented on a robotic system consisting of three Mobot modules for a total of twelve degrees of freedom using a Fourier series representation for the periodic motion of each joint. The gait implementation allows robotic modules to perform synchronized gaits with little or no communication with each other making it scalable to increasing numbers of modules. The coefficients of the Fourier series are optimized by a genetic algorithm to find gaits which move the robot cluster quickly and efficiently across flat terrain. Simulated and experimental results show that the optimized gaits can have over twice as much speed as randomly generated gaits.

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