• Journal of Korean Association for Spatial Structures
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• v.3 no.4 s.10
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• pp.59-67
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• 2003

Genetic algorithm is one of the best ways to solve a discrete variable optimization problem. Genetic algorithm tends to thrive in an environment in which the search space is uneven and has many hills and valleys. In this study, genetic algorithm is used for solving the design problem of gable structure. The design problem of frame structure has some special features(complicate design space, many nonlinear constrants, integer design variables, termination conditions, special information for frame members, etc.), and these features must be considered in the formulation of optimization problem and the application of genetic algorithm. So, 'FRAME operator', a new genetic operator for solving the frame optimization problem effectively, is developed and applied to the design problem of gable structure. This example shows that the new opreator has the possibility to be an effective frame design operator and genetic algorithm is suitable for the frame optimization problem.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.10
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• International Journal of Industrial Entomology and Biomaterials
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• v.2 no.1
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• pp.79-81
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• 2001

A study was conducted to establish the degree of genetic divergence between different hybrid forms and rearing conditions through estimation of the minimum number of genes (allelic pairs) differentiating parents in terms of specific quantitative traits. It was established that the minimum gene numbers differentiating parental lines in the inheritance of cocoon was 1, of cocoon shell weight- between 1 and 2, and of silk filament length- between 2 and 3. The variability in the specific genetic parameter could be explained by the reliability of the statistical-and-genetic method used and the expression of genes affecting the formation of each of the characters tested. Gene expression, in its turns is conditioned both by the gene interaction within the genotypes and the different genotype response to environmental change. To go deep in the problem, experiments should be conducted under strictly controlled conditions, reducing the mathematical-and-genetic analysis to a physiological levels and hence to analyse the genetic nature of the specific quantitative character formation and its genetic control.

• Proceedings of the Korea Environmental Mutagen Society Conference
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• 2002.11a
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• pp.196-196
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• 2002

• BMB Reports
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• v.37 no.1
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• pp.11-27
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• 2004

The introduction of molecular markers in genetic analysis has revolutionized medicine. These molecular markers are genetic variations associated with a predisposition to common diseases and individual variations in drug responses. Identification and genotyping a vast number of genetic polymorphisms in large populations are increasingly important for disease gene identification, pharmacogenetics and population-based studies. Among variations being analyzed, single nucleotide polymorphisms seem to be most useful in large-scale genetic analysis. This review discusses approaches for genetic analysis, use of different markers, and emerging technologies for large-scale genetic analysis where millions of genotyping need to be performed.

• Clinical and Experimental Pediatrics
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• v.61 no.4
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• pp.101-107
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• 2018

Recent advances in genetics have determined that a number of epilepsy syndromes that occur in the first year of life are associated with genetic etiologies. These syndromes range from benign familial epilepsy syndromes to early-onset epileptic encephalopathies that lead to poor prognoses and severe psychomotor retardation. An early genetic diagnosis can save time and overall cost by reducing the amount of time and resources expended to reach a diagnosis. Furthermore, a genetic diagnosis can provide accurate prognostic information and, in certain cases, enable targeted therapy. Here, several early infantile epilepsy syndromes with strong genetic associations are briefly reviewed, and their genotype-phenotype correlations are summarized. Because the clinical presentations of these disorders frequently overlap and have heterogeneous genetic causes, next-generation sequencing (NGS)-based gene panel testing represents a more powerful diagnostic tool than single gene testing. As genetic information accumulates, genetic testing will likely play an increasingly important role in diagnosing pediatric epilepsy. However, the efforts of clinicians to classify phenotypes in nondiagnosed patients and improve their ability to interpret genetic variants remain important in the NGS era.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.229.2-230
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• 2003

Among the aldehydes derived from lipid peroxidation, 4-hydroxynonenal (HNE) that can be produced from arachidonic acids. linoleic acids, or their hydroperoxides in relatively large amounts in response to oxidative insult. Therefore, HNE might be an important mediator of Oxidative stress-induced apoptosis. To study the hypothesis that HNE may induce apoptosis, we estimated cytotoxicity of HNE on YPEN-1 rat prostatic endothelial cells. (omitted)

• Proceedings of the Zoological Society Korea Conference
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• 2001.10a
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• pp.250.2-251
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• 2001

No Abstract, See Full Text

• Proceedings of the Korea Environmental Mutagen Society Conference
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• 2004.11a
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• pp.138-138
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• 2004