• Journal of Crop Science and Biotechnology
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• v.11 no.1
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• pp.63-68
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• 2008

The induced genetic divergence was estimated in 44 mutant lines of finger millet variety GPU 26, developed by single and combination treatments with gamma rays, EMS and NG using three multivariate analyses. The mutant lines were grouped into eight genetically diverse clusters by multivariate D2 and canonical analyses and 11 clusters by dendrogram grouping through Gower's similarity coefficient. The clustering pattern in these three methods was almost similar. Twelve mutant lines in D2 and 13 in the dendrogram grouping method were grouped in the parental cluster(Cluster I) indicating that they did not possess enough divergence from the parent to be classified as micromutant lines. However a large proportion of mutant lines showed divergence from the parent variety and also among themselves. No definite relationship of mutagenic origin and clustering of mutant lines were observed. The mutant lines developed from the same mutagenic treatments often grouped into different clusters indicating that each mutagenic treatment was effective in inducing diverse types of changes in the nine traits studied. The hybridization program between the divergent mutant lines GE 2-2 or GE 3-4 with GG 3-1 is expected to give promising and desirable segregants in subsequent generations. Traits such as days to 50% flowering and days to maturity had major contributions to the induced genetic divergence.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.81-86
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• 2001

one key goal of software testing is to generate a 'good' test data set, which is consideres as the most difficult and time-consuming task. This paper discusses how genetic algorithns can be used for automatic generation of test data set for software testing. We employ mutation testing to show the effectiveness of genetic algorithms (GAs) in automatic test data generation. The approach presented in this paper is different from other in that test generation process requireas no lnowledge of implementation details of a program under test. In addition, we have conducted some experiments and compared our approach with random testing which is also regarded as a black-box test generation technique to show its effectiveness.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.77-83
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• 2005

This paper deals with determination of motions of a humanoid robot using genetic algorithm. A humanoid robot has some problems of the structural instability basically. So, we have to consider the stable walking gait in gait planning. Besides, it is important to make the smoothly optimal gait for saving the electric power. A mobile robot has a battery to move autonomously. But a humanoid robot needs more electric power in order to drive many joints. So, if movements of walking joints don't maintain optimally, it is difficult for a robot to have working time for a long time. Also, if a gait trajectory doesn't have optimal state, the expected life span of joints tends to be decreased. To solve these problems, the genetic algorithm is employed to guarantee the optimal gait trajectory. The fitness functions in a genetic algorithm are introduced to find out optimal trajectory, which enables the robot to have the less reduced jerk of joints and get smooth movement. With these all process accomplished by a PC-based program, the optimal solution could be obtained from the simulation. In addition, we discuss the design consideration for the joint motion and distributed computation of the humanoid, ISHURO, and suggest its result such as the structure of the network and a disturbance observer.

• Korean Journal of Agricultural Science
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• v.42 no.4
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• pp.397-406
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• 2015

Quantitative traits are mostly controlled by a large number of genes. Some of these genes tend to have a large effect on quantitative traits in cattle and are known as major genes primarily located at quantitative trait loci (QTL). The genetic merit of animals can be estimated by genomic selection, which uses genome-wide SNP panels and statistical methods that capture the effects of large numbers of SNPs simultaneously. In practice, the accuracy of genomic predictions will depend on the size and structure of reference and training population, the effective population size, the density of marker and the genetic architecture of the traits such as number of loci affecting the traits and distribution of their effects. In this review, we focus on the structure of Hanwoo reference and training population in terms of accuracy of genomic prediction and we then discuss of genetic architecture of intramuscular fat(IMF) and marbling score(MS) to estimate genomic breeding value in real small size of reference population.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.3
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• pp.309-319
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• 2017

Objective: Holsteins are known as the world's highest-milk producing dairy cattle. The purpose of this study was to identify genetic regions strongly associated with milk traits (milk production, fat, and protein) using Korean Holstein data. Methods: This study was performed using single nucleotide polymorphism (SNP) chip data (Illumina BovineSNP50 Beadchip) of 911 Korean Holstein individuals. We inferred each genomic estimated breeding values based on best linear unbiased prediction (BLUP) and ridge regression using BLUPF90 and R. We then performed a genome-wide association study and identified genetic regions related to milk traits. Results: We identified 9, 6, and 17 significant genetic regions related to milk production, fat and protein, respectively. These genes are newly reported in the genetic association with milk traits of Holstein. Conclusion: This study complements a recent Holstein genome-wide association studies that identified other SNPs and genes as the most significant variants. These results will help to expand the knowledge of the polygenic nature of milk production in Holsteins.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.1-8
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• 1998

This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.

• Journal of Korean Association for Spatial Structures
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• v.5 no.3 s.17
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• pp.117-122
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• 2005

This paper investigated the optimum design of truss structures based on Genetic Algorithms (GA's). With GA's characteristic of running side by side, the overall optimization and feasible operation, the optimum design model of truss structures was established. Elite models were used to assure that the best units of the previous generation had access to the evolution of current generation. Using of non-uniformity mutation brought the obvious mutation at earlier stage and stable mutation in the later stage; this benefited the convergence of units to the best result. In addition, to avoid GA's drawback of converging to local optimization easily, by the limit value of each variable was changed respectively and the genetic operation was performed two times, so the program could work more efficiently and obtained more precise results. Finally, by simulating evolution process of nature biology of a kind self-organize, self-organize, artificial intelligence, this paper established continuous structural optimization model for ten bars cantilever truss, and obtained satisfactory result of optimum design. This paper further explained that structural optimization is practicable with GA's, and provided the theoretic basis for the GA's optimum design of structural engineering.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.923-928
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• 1993

Genetic algorithms are powerful optimization methods based on the mechanism of natural genetics and natural selection. Genetic algorithms reduce chance of searching local optima unlike most conventional search algorithms and especially show good performances in complex nonlinear optimization problems because they do not require any information except objective function value. This paper presents a new model based on sexual reproduction in nature. In the proposed Sexual Reproduction model(SR model), individuals consist of the diploid of chromosomes, which are artificially coded as binary string in computer program. The meiosis is modeled to produce the sexual cell(gamete). In the artificial meiosis, crossover between homologous chromosomes plays an essential role for exchanging genetic informations. We apply proposed SR model to optimization of the design parameters of Single-sided Linear Induction Motor(SLIM). Sequential Unconstrained Minimization Technique(SUMT) is used to transform the nonlinear optimization problem with many constraints of SLIM to a simple unconstrained problem, We perform optimal design of SLIM available to FA conveyer systems and discuss its results.

• Journal of Plant Biotechnology
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• v.37 no.4
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• pp.357-369
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• 2010

Cymbidium is horticulturally important and has been one of the most commercially successful orchid plants as well as cut flowers around the world including Korea. Up to now, a huge number of elite Cymbidium cultivars have been released on the commercial market via cross-hybridization, mutation and polyploidization breeding techniques. To investigate on breeding system in Cymbidium, we inquired the brief history and techniques of breeding and the current status on Cymbidium breeding in Korea. Also, the general propagation process of elite Cymbidium lines via tissue culture should be presented. However, the slow process of conventional breeding and the lack of useful genes in Cymbidium species delays the introduction of new cultivars to the commercial market. To solve these limitations, efficient regeneration and genetic transformation systems should be established in the improvement of Cymbidium breeding program. During the last several decades, some progress has been made in tissue culture and genetic transformation in Cymbidium species. We review the recent status of tissue culture and genetic transformation systems in Cymbidium plants.

• Journal of Plant Biotechnology
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• v.37 no.4
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• pp.370-378
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• 2010

This review describes the stratagies of development of virus-resistant Alstroemeria plants using the genetic modification system. Despite of increasing of its importance in cut flower market, improvements of some horticultuirally important traits such as fragrance, long vase-life, virus resistance and tolerance against abiotic stresses are lack of the breeding program in Alstroemeria. Of these traits, virus-resistance is quite difficult to develop in Alstroemeria plants due to the limitations of genetic variation in the existed germplasm. To extend the genetic variation, plant biotechnological techniques such as genetic transformation and tissue culture should be combined to develop virus-resistant line in Alstroemeria. In this review, several strategies for the generation of virus-resistance by using natural resistance genes, pathogen-derived genes and other sources including pathogen-derived proteins, virus-specific antibodies and ribosome-inactivating proteins are presented. Also, brief histories of breeding, tissue culture, and transformation system in Alstroemeria plants are described to inderstand of the application of transgenic approach for the development of virus-resistance in Alstroemeria species.