• Molecules and Cells
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• v.20 no.2
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• pp.201-209
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• 2005

We have constructed an AFLP-based linkage map of Japanese red pine (Pinus densiflora Siebold et Zucc.) using haploid DNA samples of 96 megagametophytes from a single maternal tree, selection clone Kyungbuk 4. Twenty-eight primer pairs generated a total of 5,780 AFLP fragments. Five hundreds and thirteen fragments were verified as genetic markers with two alleles by their Mendelian segregation. At the linkage criteria LOD 4.0 and maximum recombination fraction 0.25(${\theta}$), a total of 152 markers constituted 25 framework maps for 19 major linkage groups. The maps spanned a total length of 2,341 cM with an average framework marker spacing of 18.4 cM. The estimated genome size was 2,662 cM. With an assumption of equal marker density, 82.2% of the estimated genome would be within 10 cM of one of the 230 linked markers, and 68.1% would be within 10 cM of one of the 152 framework markers. We evaluated map completeness in terms of LOD value, marker density, genome length, and map coverage. The resulting map will provide crucial information for future genomic studies of the Japanese red pine, in particular for QTL mapping of economically important breeding target traits.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.2
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• pp.123-127
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• 2000

The objective of this study was to develop a linkage map of soybean under the genetic background of Korean soybean. A set of 89 F/sub 5/ lines was developed from a cross between 'Pureunkong', which was released for soy-bean sprout, and 'Jinpumkong 2', which had no beany taste in seed due to lack of lipoxygenase 1, 2, and 3. A linkage map was constructed for this population with a set of 113 genetic markers including 7 restriction fragment length polymorphism (RFLP) markers, 79 randomly amplified polymorphic DNA (RAPD) markers, 24 simple sequence repeat(SSR) markers, and 3 morphological markers. The map defined approximately 807.4 cM of the soybean genome comprising 25 linkage groups with 98 polymorphic markers. Fifteen markers remained unlinked. Seventeen linkage groups identified here could be assigned to the respective 13 linkage groups in the USDA soybean genetic map. RFLP and SSR markers segregated at only single genetic loci. Fourteen of the 25 linkage groups contained at least one SSR marker locus. Map positions of most of the SSR loci and their linkages with RFLP markers were consistent with previous reports of the USDA soybean linkage groups. For RAPD, banding patterns of 13 decamer primers showed independent segregations at two or more marker loci for each primer. Only the segregation at op Y07 locus was expressed with codominant manner among all RAPD loci. As the soybean genetic map in our study is more updated, molecular approaches of agronomically important genes would be useful to improve Korean soybean improvement.

• BMB Reports
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• v.35 no.5
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• pp.465-471
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• 2002

From fruiting bodies of L. edodes strain L-54, single-spore isolates (SSIs) were collected. Two parental types of L-54 were regenerated via monokaryotization. By means of random-amplified polymorphic DNA (RAPD), DNA samples from L-54, its two parental types, and 32 SSIs were amplified with arbitrary primers. Dedikaryotization was demonstrated, and 91 RAPD-based molecular markers were generated. RAPD markers that were segregated at a 1:1 ratio were used to construct a linkage map of L. edodes. This RAPD-linkage map greatly enhanced the mapping of other inheritable and stable markers [such as those that are linked to a phenotype (the mating type), a known gene (priA) and a sequenced DNA fragment (MAT)] with the aid of mating tests, bulked-segregant analysis, and PCR-single-strand conformational polymorphism. These markers comprised a genetic map of L. edodes with 14 linkage groups and a total length of 622.4 cM.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.4
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• pp.297-302
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• 2003

Genetic linkage maps serve the plant geneticist in a number of ways, from marker assisted selection in plant improvement to map-based cloning in molecular genetic research. Genetic map based upon DNA polymorphism is a powerful tool for the study of qualitative and quantitative traits in crops. The objective of this study was to develop genetic linkage map of soybean using the population derived from the cross of Korean soybean cultivar 'Kwangkyo, and wild accession 'IT182305'. Total 1,000 Operon random primers for RAPD marker, 49 combinations of primer for AFLP marker, and 100 Satt primers for SSR marker were used to screen parental polymorphism. Total 341 markers (242 RAPD, 83 AFLP, and 16 SSR markers) was segregated in 85 $\textrm{F}_2$ population. Forty two markers that shown significantly distorted segregation ratio (1:2:1 for codominant or 3:1 for domimant marker) were not used in mapping procedure. A linkage map was constructed by applying the computer program MAPMAKER/EXP 3.0 to the 299 marker data with LOD 4.0 and maximum distance 50 cM. 176 markers were found to be genetically linked and formed 25 linkage groups. Linkage map spanned 2,292.7 cM across all 25 linkage groups. The average linkage distance between pair of markers among all linkage groups was 13.0 cM. The number of markers per linkage group ranged from 2 to 55. The longest linkage group 3 spanned 967.4 cM with 55 makers. This map requires further saturation with more markers and agronomically important traits will be joined over it.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2005.11a
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• pp.299-303
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• 2005

In the present study, in order to improve precision of the component characteristic maps generated by the scaling method, a map generation method which can produce a compressor map from some experimental performance data using GAs(Genetic Algorithms) was proposed. However, in case of the proposed map generation method only using GAs, because it has a drawback for estimating correctly the surge points and the choke points of the compressor map, a modified GAs method was additionally proposed through complementally use of the scaling method to determine obviously those points of the compressor map.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.385-391
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• 2013

Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.

• Asian-Australasian Journal of Animal Sciences
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• v.12 no.7
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• pp.1129-1134
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• 1999

The rapid development of the sheep genetic linkage map over the last five years has given us the ability to follow the inheritance of chromosomal regions. Initially this powerful resource was used to find markers linked to monogenic traits but there is now increasing interest in using the genetic linkage map to define the complex of genes that control multigenic production traits. Of particular interest are those production traits that are difficult to measure and select for using classical quantitative genetic approaches. These include resistance to disease where a disease challenge (necessary for selection) poses too much risk to valuable stud animals and meat and carcass qualities which can be measured only after the animal has been slaughtered. The goal for the new millennium will be to fully characterise the genetic basis of multigenic production traits. The genetic linkage map is a vital tool required to achieve this.

• Journal of the Korean Society of Propulsion Engineers
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• v.8 no.3
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• pp.44-52
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• 2004

In this study, a component map generation method using experimental data and the genetic algorithms are newly proposed. In order to generate the performance map for components of this engine, after obtaining engine performance data through many experimental tests, and then the third order equations which have relationships the mass flow function the pressure ratio and the isentropic efficiency as to the engine rotational speed were derived by using the genetic algorithms. A steady-state performance analysis was performed with the generated maps of the compressor by the commercial gas turbine performance analysis program GASTURB. In comparison, it was found that the component maps can be generated from the experimental test data by using the genetic algorithms, and it was confirmed that the analysis results using the generated maps were very similar to those using the scaled maps from the GASTURB.

• Journal of Korea Game Society
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• v.17 no.5
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• pp.123-132
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• 2017

The path-finding algorithm refers to an algorithm for navigating the route order from the current position to the destination in a virtual world in a game. The conventional path-finding algorithm performs graph search based on cost such as A-Star and Dijkstra. A-Star and Dijkstra require movable node and edge data in the world map, so it is difficult to apply online games with lots of map data. In this paper, we provide a Heuristic-based Genetic Algorithm Path-finding(HGAP) using Genetic Algorithm(GA). Genetic Algorithm is a path-finding algorithm applicable to game with variable environment and lots of map data. It seek solutions through mating, crossing, mutation and evolutionary operations without the map data. The proposed algorithm is based on Binary-Coded Genetic Algorithm and searches for a path by performing a heuristic operation that estimates a path to a destination to arrive at a destination more quickly.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.375-381
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• 2007

Cognitive map (CM) has long been used as an effective way of constructing the human thinking process. In literature regarding CM, a number of successful researches were reported, where CM based what-if analysis could enhance firm's performance. However, there exit very few researches investigating the CM generation method. Therefore this study proposes a GENCOM (Genetic Algorithm based Cognitive Map Generator). In this model combined with CM and GA, GA will find the optimal weight and input vector so that the CM generation. To empirically prove the effectiveness of GENCOM, we collected valid questionnaires from expert in S/W sales cases. Empirical results showed that GENCOM could contribute to effective CM simulation and very useful method to extracting the tacit knowledge of sales experts.