• Korean Journal of Agricultural Science
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• v.32 no.1
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• pp.53-61
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• 2005

Many bovine genetic diseases are currently unidentified in Korea because of the relatively low monitoring systems in the livestock farms. The molecular detection system using PCR-RFLP of two genetic diseases, namely Band 3 (Erythrocyte Membrane Protein Band III) and CHS (Chediak-Higashi Syndrome), have been identified in Japan and used for screening large number of cattle whether each individual has the genetic disease or not. Using the 22 unrelated Korean cattle (Hanwoo) individuals, molecular detection system based on PCR-RFLP have been investigated, which can be distinguishable carriers for the genetic diseases. Even though we could not found the causative mutations for two genetic diseases, the PCR-RFLP techniques used in this study are very valuable for the screening the genetics diseases in Korean cattle, especially for the proven or candidate bulls.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.1.2-1
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• 2001

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.11
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• pp.1548-1553
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• 2014

Four Korean native cattle (KNC) breeds-Hanwoo, Chikso, Heugu, and Jeju black-are entered in the Domestic Animal Diversity Information System of the United Nations Food and Agriculture Organization (FAO). The objective of this study was to assess the genetic diversity, phylogenetic relationships and population structure of these KNC breeds (n = 120) and exotic breeds (Holstein and Charolais, n = 56). Thirty microsatellite loci recommended by the International Society for Animal Genetics/FAO were genotyped. These genotypes were used to determine the allele frequencies, allelic richness, heterozygosity and polymorphism information content per locus and breed. Genetic diversity was lower in Heugu and Jeju black breeds. Phylogenetic analysis, Factorial Correspondence Analysis and genetic clustering grouped each breed in its own cluster, which supported the genetic uniqueness of the KNC breeds. These results will be useful for conservation and management of KNC breeds as animal genetic resources.

• Journal of Genetic Medicine
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• v.5 no.2
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• pp.89-93
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• 2008

"Personalized medicine," the goal of which is to provide better clinical care by applying patient's own genomic information to their health care is a global challenge for the $21^{st}$ century "genomic era." This is especially true in Korea, where provisions for clinical genetic services are inadequate for the existing demand, let alone future demands. Genomics-based knowledge and tools make it possible to approach each patient as a unique biological individual, which has led to a paradigm-shift in medical practice, giving it more of a predictive focus as compared with current treatment oriented approach. With recent advancements in genomics, many genetic tests, such as susceptibility genetic tests, have been developed for both rare single gene diseases and more common multifactorial diseases. Indeed, genetic tests for presymtomatic individuals and genetic tests for drug response have become widely available, and personalized medicine will face the challenge of assisting patients who use such tests to make appropriate and wise use of genetic risk assessment. A major challenge of genomic medicine lies in understanding and communicating disease risk in order to facilitate and support patients and their families in making informed decisions. Establishment of a health care system with provisions for genetic counseling as an integral part of health care service, in addition to genomic literacy of health care providers, is vital to meet this growing challenge. Realization of the promise of personalized medicine in the era of genomics for improvement of health care is dependent on further development of next generation sequencing technology and affordable sequencing test costs. Also necessary will be policy development concerning the ethical, legal and social issues of genomic medicine and an educated and ready medical community with clinical practice guidelines for genetic counseling and genetic testing.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.85-92
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• 2005

In this Paper, we develope an effective web-based learning contents using interactive genetic algorithms with modified mutation operation. In the interactive genetic algorithm, reciprocal exchange mutation is used. But. we modify the mutation operator to improve the learning effects. The new web-based learning contents using interactive genetic algorithm provide the dynamic learning contents providing and real-time test system. Especially, learners can execute the interactive genetic algorithm according to the learners' characters and interests to select the efficient learning environments and contents sequences.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.612-618
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• 1998

The success of a fuzzy logic control system solving any given problem critically depends on the architecture of th network. Various attempts have been made in optimizing its structure its structure using genetic algorithm automated designs. In a regular genetic algorithm , a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. This paper presents a new approach to structurally optimized designs of a fuzzy model. We use a messy genetic algorithm, whose main characteristics is the variable length of chromosomes. A messy genetic algorithms used to obtain structurally optimized fuzzy models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the exampled of a cart-pole balancing.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.4
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• pp.469-478
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• 2012

This study aims to discover the optimal pipe layout for a ship, which generally needs a lot of time, efforts and experiences. Genetic algorithm was utilized to search for the optimum. Here the optimum stands for the minimum pipe length between two given points. Genetic algorithm is applied to planar pipe layout problems to confirm plausible and efficiency. Sub-programs are written to find optimal layout for the problems. Obstacles are laid in between the starting point and the terminal point. Pipe is supposed to bypass those obstacles. Optimal layout between the specified two points can be found using the genetic algorithm. Each route was searched for three case models in two-dimensional plane. In consequence of this, it discovered the optimum route with the minimized distance in three case models. Through this study, it is possible to apply optimization of ship pipe route to an actual ship using genetic algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.261-268
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• 2020

Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

• Animal cells and systems
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• v.1 no.4
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• pp.619-627
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• 1997

Maintenance of polymorphism in a two-locus system with two alleles under stabilizing selection has been tested by Monte-Carlo simulation. The effect of each allele was additive. Only gene x environment interactions and degree of genetic linkage between loci were considered. There were no other evolutionary forces acting except stabilizing selection. Fixation rates were influenced by the extent of environmental change and the degree of genetic linkage. In most cases, stabilizing selection depleted genetic variability when two loci have a lower degree of linkage (10 cM). When two loci are closely linked (0.1 cM), however, stabilizing selection promoted balanced heterozygotes in changing environments. Thus, environment-dependent selection and recombination rate are important parameters which should be incorporated into mechanisms of maintenance of genetic variability.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.26 no.4
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• pp.23-30
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• 2003

This study develops an efficient heuristic which is based on genetic approach for AGVs flow path layout problem. The suggested solution approach uses a algorithm to replace two 0-1 integer programming models and a branch-and-bound search algorithm. Genetic algorithms are a class of heuristic and optimization techniques that imitate the natural selection and evolutionary process. The solution is to determine the flow direction of line in network AGVs. The encoding of the solutions into binary strings is presented, as well as the genetic operators used by the algorithm. Genetic algorithm procedure is suggested, and a simple illustrative example is shown to explain the procedure.