• Journal of Plant Biology
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• v.37 no.2
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• pp.141-149
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• 1994

Levels of genetic diversity, population genetic structure, and gene flow in Hosta yingeri, a herbaceous perennial endemic to Taehuksan, Sohuksan, and Hong Islands, were investigated. Starch gel electrophoresis was conducted on leaves for 101 plants collected from three populations. Although the distribution of thespecies is restricted in the islands, it maintains high levels of genetic variatin; 64% of polymorphic loci in at least one population (Ps), the mean number of alleles per locus (Ap) of 1.92, and the mean effective number of alleles per locus (Aep) of 1.52. Overall, mean genetic diversity (Hep=0.250) was substantially higher than mean estimate for species with very similarlife history traits (0.102). Large populaton size, the persistence of multiple generations within populations, high fecundity, predominantly outcrossing breeding system, large size of pollinator visitation areas may be explanatory factors contributing the higher level of genetic diversity maintained within populations. Analysis of fixation indices showed an overall slight excess of heterozygotes (mean FIS=-0.066) relative to Hardy-Weinberg expectations, which may in part be due to the near self-incompatible breeding system in the species. Significant differences in allele frequencies among populaitns were found for 14 out of 16 polymorphic loci (P<0.05). Slightly more than 80% of the total variation in the species was common to all populations (GST=0.198). As expected, indirect estimate of the number of migrants per generation (Nm=0.45, calculated from mean GST) and nine private alleles found in the three populations indicate that gene movement among three isolated island populations was low.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.3-5
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• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.04a
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• pp.61-61
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• 2019

In any crop breeding program Selection and use of genetically diverse genotypes to develop cultivars with a broad genetic base is important. Molecular markers play a major role in selecting diverse genotypes. Molecular breeding programs of the crop can be made more efficient by use of molecular markers. The present study was done with an aim of analyzing genetic diversity and the population structure in 24 accessions of sunflower (Helianthus annus L.) from Kenya genetic diversity using 35 EST-SSR and gSSR primers.Out of the 35 markers 3 were not polymorphic as they indicated Polymorphic Information content( PIC) of value 0.00 and so the data analysis was done using 32 markers . The 32 set of markers used produced 29 alleles ranging from 2 to 7with a mean of 3.0 alleles per locus.The average value of polymorphic information contents(PIC) were 0.3 .Genetic diversity analysis using these markers revealed 3 major clusters. This result could be useful for designing strategies to make elite hybrid and inbreeding of crossing block for breeding and future molecular breeding programs to make elite variety.

• Journal of Animal Science and Technology
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• v.62 no.6
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• pp.790-800
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• 2020

The Yeonsan Ogye (YO) chicken is a natural heritage of Korea, characterized by black feathers, skin, bones, eyes, and comb. The purebred of YO population has been reared under the natural mating system with no systematic selection and breeding plan. The purpose of this study was to identify the genetic diversity and find the optimal number of population sub-division using 12 polymorphic microsatellite (MS) markers to construct a pedigree-based breeding plan for the YO population. A total of 509 YO birds were used for this study. Genetic diversity and population structure analysis were conducted based on the MS marker genotype information. The overall average polymorphic information content value and expected heterozygosity of the population were 0.586, and 0.642, respectively. The K-mean cluster analysis based on the genetic distance result confirmed that the current YO population can be divided into three ancestry groups. Individuals in each group were evaluated based on their genetic distance to identify the potential candidates for a future breeding plan. This study concludes that a future breeding plan with known pedigree information of selected founder animals, which holds high genetic diversity, could be the best strategy to ensure the conservation of the Korean YO chicken population.

• ALGAE
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• v.37 no.2
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• pp.135-147
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• 2022

The red algal genus Chondrus have long been used as raw materials for carrageenan and dietary fiber in health foods. Despite the importance of genetic information in safeguarding natural seaweed resources, knowledge of the population genetics of Chondrus in the northwest Pacific is limited. In this study, genetic diversity and phylogeographic structure of 45 populations (777 specimens) of Chondrus from Korea, China, and Japan were evaluated based on mitochondrial COI-5P gene sequences, and phylogenetic relationships were confirmed based on plastid rbcL gene sequences. Molecular analyses assigned the specimens in this study to three Chondrus species: C. nipponicus, C. ocellatus, and C. giganteus; phenotype-based species classification was impossible owing to their high morphological plasticity. We found moderate intraspecific genetic diversity and a shallow phylogeographic structure in both for C. nipponicus and C. ocellatus, and low intraspecific genetic diversity in C. giganteus. Each of the three species exhibited high-level intraspecific gene flow among regions based on the most common haplotypes (CN1 for C. nipponicus, CO1 for C. ocellatus, and CG1 for C. giganteus). Our comprehensive genetic information provides insights into the phylogeographic patterns and intraspecific diversity of the economically important Chondrus species. It also highlights the need to conserve existing natural Chondrus resources through continuous monitoring of genetic diversity and phylogeographic pattern.

• Korean Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.234-241
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• 1996

Recently, Genetic Algorithms(GAs), which consist of genetic operators named selection crossover and mutation, are widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GAs are very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GAs. So, they can be easily applicable to wide territory of design optimization problems. Also, virtue to multi-point search procedure, they have higher probability of convergence to global optimum compared with traditional techniques which take one-point search method. The introduction of basic theory on GAs, and the application examples in combination optimization of ten-member truss structure are presented in this paper.

• Steel and Composite Structures
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• v.25 no.4
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• pp.473-484
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• 2017

In this study a seismic retrofit scheme for a building structure was presented using steel plate slit dampers. The energy dissipation capacity of the slit damper used in the retrofit was verified by cyclic loading test. Genetic algorithm was applied to find out the optimum locations of the slit dampers satisfying the target displacement. The seismic retrofit of the model structure using the slit dampers was compared with the retrofit with enlarging shear walls. A simple damper distribution method was proposed using the capacity spectrum method along with the damper distribution pattern proportional to the inter-story drifts. The validity of the simple story-wise damper distribution procedure was verified by comparing the results of genetic algorithm. It was observed that the capacity-spectrum method combined with the simple damper distribution pattern leaded to satisfactory story-wise distribution of dampers compatible with the optimum solution obtained from genetic algorithm.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.5
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• pp.378-386
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• 2017

In this paper, a method is presented for the optimal design of submarine pressure hull structures by taking advantage of genetic algorithm techniques. The objective functions and design constraints in the process of structural optimization are based on the ultimate limit states of hull structures. One of the benefits associated with the utilization of genetic algorithm is that the optimization process can be completed within short generations of design variables for the pressure hull structure model. Applied examples confirm that the proposed method is useful for the optimal design of submarine pressure hull structures. Details of the design procedure with applied examples are documented. The conclusions and insights obtained from the study are summarized.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.5
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• pp.93-99
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• 2000

In many dynamic structural optimization problems, the goal is to reduce the total weight of the structure without causing the resonance. Up to now, gradient informations(i.e., design sensitivity) have been used to achieve the goal. For some class of dynamic problems, especially coalescent eigenvalue Problems with multiobjective optimization, the design sensitivity analysis is too much complicated mathematically and numerically. Therefore, this article proposes a new technique fur structural dynamic modification using a mode modification method with Genetic Algorithm(GA). In GA formulation, fitness is defined based on penalty function approach. Design variables are iteratively improved by using genetic algorithm. Two numerical examples are shown, (ⅰ) a cantilevered plate, and (ⅱ) H-shaped structure. The results demonstrate that the proposed method is highly efficient.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.158-165
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• 1999

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.