• Journal of Korean Society of Transportation
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• 제22권2호
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• pp.43-54
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• 2004

This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

• Journal of Plant Biotechnology
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• 제6권1호
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• pp.1-7
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• 2004

Forestry sector has witnessed some unprecedented events in the recent past both in terms of galloping biotechnological developments and heated environmental debates over risks associated with release of transgenic trees. Improvements in the in vitro propagation techniques has made it possible to develop tissue culture based plant regeneration protocols just for about any tree species. And with the inclusion of every new species within the realms of tissue culture technology, it becomes a candidate for genetic improvement through recombinant DNA technology, the so called genetic engineering. Poplars and their hybrids serve as the model tree species on which most of the genetic transformation work as been carried out. A lot of work has also gone in genetic transformation of fruit trees and trees of horticultural interests. Trees have been successfully transformed for traits ranging from reduction of length of juvenile phase to alteration of tree architecture to altering wood quality by lignin and cellulose modification. More-over trees have been genetically engineered successfully to combat various types of insect pests and pathogens causing diseases. But all these developments have ignited controversies over the possible benefits and risks associated with transgenic plantations by various environmental agencies and activists. Solutions to most of these concerns can be found out with more intensive prioritized research.

• Transactions of the Korean Society of Mechanical Engineers A
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• 제24권6호
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

• Journal of the Korean Operations Research and Management Science Society
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• 제26권4호
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• pp.133-141
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• 2001

Rapid development in computer and network technology these days has created in environment in which decisions for manufacturing companies can be made in a much broader perspective. Especially, better decisions on production and distribution planning(PDP) problems can be made laking advantage of real time information from all the parties concerned. However, since the PDP problem-a core part of the supply chain management- is known to be the so-called NP-hard problem, so heuristic methods are dominantly used to find out solutions in a reasonable time. As one of those heuristic techniques, many previous studios considered genetic a1gorithms. A standard genetic a1gorithm applies rules of reproduction, gene crossover, and mutation to the pseudo-organisms so the organisms can pass along beneficial and survival-enhancing trails to a new generation. When it comes to representing a chromosome on the problem, it is hard to guarantee an evolution of solutions through classic a1gorithm operations alone, for there exists a strong epitasis among genes. To resolve this problem, we propose a hybrid genetic a1gorithm based on Silver-Meal heuristic. Using IMS-TB(Intelligent Manufacturing System Test-bed) problem sets. the good performance of the proposed a1gorithm is demonstrated.

• Journal of the Korean Institute of Intelligent Systems
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• 제19권4호
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• pp.580-585
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• 2009

This paper introduces GP(Genetic Programming) based corner detectors for an image processing. Various empirical algorithms have been studied to improve computational speed and accuracy including typical approaches, such as Harris and SUSAN. The these techniques are highly efficient, because properties of corner points are inspected and reflected into the algorithms. However these approaches are limited in discovering an innovative algorithm. In this study, we try to discover a more efficient technique by creating corner detector automatically using evolution of GP. The proposed method is compared to the existing corner detectors for test images.

• Molecules and Cells
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• 제39권10호
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• pp.722-727
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• 2016

Cardiomyopathy is a major cause of death worldwide. Based on pathohistological abnormalities and clinical manifestation, cardiomyopathies are categorized into several groups: hypertrophic, dilated, restricted, arrhythmogenic right ventricular, and unclassified. Dilated cardiomyopathy, which is characterized by dilation of the left ventricle and systolic dysfunction, is the most severe and prevalent form of cardiomyopathy and usually requires heart transplantation. Its etiology remains unclear. Recent genetic studies of single gene mutations have provided significant insights into the complex processes of cardiac dysfunction. To date, over 40 genes have been demonstrated to contribute to dilated cardiomyopathy. With advances in genetic screening techniques, novel genes associated with this disease are continuously being identified. The respective gene products can be classified into several functional groups such as sarcomere proteins, structural proteins, ion channels, and nuclear envelope proteins. Nuclear envelope proteins are emerging as potential molecular targets in dilated cardiomyopathy. Because they are not directly associated with contractile force generation and transmission, the molecular pathways through which these proteins cause cardiac muscle disorder remain unclear. However, nuclear envelope proteins are involved in many essential cellular processes. Therefore, integrating apparently distinct cellular processes is of great interest in elucidating the etiology of dilated cardiomyopathy. In this mini review, we summarize the genetic factors associated with dilated cardiomyopathy and discuss their cellular functions.

• Journal of KSNVE
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• 제10권5호
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• pp.783-791
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• 2000

This describes a study on the support location optimizations of the beams using the genetic algorithm and the sensitivity analysis. The genetic algorithm is a probabilistic method searching the optimum at several points simultaneously and requiring only the values of the object and constraint functions. It has therefore more chances to find the global solution and can be applied to the various problems. Nevertheless, it has such a shortcoming that it takes too many calculations, because it is ineffective in local search. While the traditional method using sensitivity analysis is of great advantage in searching the near optimum. thus the combination of the two techniques will make use of the individual advantages, that is, the superiority in global searching form the genetic algorithm and that in local searching form the sensitivity analysis. In this thesis, for the practical applications, the analysis is conducted by FEB ; and as the shapes of structures are taken as the design variation, it requires re-meshing for every analysis. So if it is not properly controlled, the result of the analysis is affected and the optimized solution amy not be the real one. the method is efficiently applied to the problems which the traditional methods are not working properly.

• Journal of the Korean Institute of Intelligent Systems
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• 제20권6호
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• pp.780-785
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• 2010

This paper introduces GP(Genetic Programming) based color detection model for an object detection and tracking. Existing color detection methods have used linear/nonlinear transformatin of RGB color-model and improved color model for illumination variation by optimization or learning techniques. However, most of cases have difficulties to classify various of colors because of interference of among color channels and are not robust for illumination variation. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various colors and images with different lighting conditions.

• Journal of the Korea Society of Computer and Information
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• 제11권4호
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• pp.119-125
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• 2006

Distributed Systems can be defined as set of computing resources connected by computer network. One of the most significant techniques in optimization problem domains is parallel genetic algorithms, which are based on distributed systems. Since the status of dynamic network environments such as Internet and mobile computing. can be changed continually, it must not be efficient on the dynamic environments to solve an optimization problem using previous parallel genetic algorithms themselves. In this paper, we propose the effective technique, in which the parallel genetic algorithm can be used efficiently on the dynamic network environments.

• Structural Engineering and Mechanics
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• 제68권5호
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• pp.549-561
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• 2018

Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.