• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.93.4-93
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• 2002

When the GA is applied to optimization problems, it is important to maintain the diversity in designing generation alternation model. Generally, when the diversity is not fully maintained, it is difficult to find good solution, and it is easy to stagnate the early convergenece. In this paper, we propose the Elite Correlation Selection operator (ECS) as a new selection operator for survival. This selection operator aims to keep the diversity of populations and contributes the high searching ability. This selection operator is an extension of selection operator for survival in the Minimal Generation Gap (MGG). In the selection for survival, this selection operator selects one elite individual...

• Journal of Embryo Transfer
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• v.28 no.2
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• pp.79-85
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• 2013

The recent development in genetic assisted selection (combining traditional- and genome assisted selection method) and reproduction technologies will allow multiplying elite cow in Hanwoo small farm. This review describes the new context and corresponding needs for genome assisted selection schemes and how reproductive technologies can be incorporated to get more genetic gain for cow genetic improvement in Hanwoo. New improved massive phenotypes and pedigree information are being generated from commercial farm sector and these are allowing to do genetic evaluation using BLUP to get elite cows in Korea. Moreover cattle genome information can now be incorporated into breeding program. In this context, this review will discuss about combining the reproductive techniques (Multiple Ovulation Embryo Transfer; MOET) and genome assisted selection method to get more genetic gain in Hanwoo breeding program. Finally, how these technologies can be used for multiplication of elite cow in small farm was discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1802-1814
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• 2012

In order to guide users to select the most optimal access network in heterogeneous wireless networks, a network selection algorithm is proposed which is designed based on multi-objective discrete particle swarm optimization (Multi-Objective Discrete Particle Swarm Optimization, MODPSO). The proposed algorithm keeps fast convergence speed and strong adaptability features of the particle swarm optimization. In addition, it updates an elite set to achieve multi-objective decision-making. Meanwhile, a mutation operator is adopted to make the algorithm converge to the global optimal. Simulation results show that compared to the single-objective algorithm, the proposed algorithm can obtain the optimal combination performance and take into account both the network state and the user preferences.

• Structural Engineering and Mechanics
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• v.70 no.5
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.79-86
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• 2017

This research focused on deciding optimal manufacturing WIP (Work-In-Process) limit for a small production system. Reducing WIP leads to stable capacity, better manufacturing flow and decrease inventory. WIP is the one of the important issue, since it can affect manufacturing area, like productivity and line efficiency and bottlenecks in manufacturing process. Several approaches implemented in this research. First, two strategies applied to decide WIP limit. One is roulette wheel selection and the other one is elite strategy. Second, for each strategy, JIT (Just In Time), CONWIP (Constant WIP), Gated Max WIP System and CWIPL (Critical WIP Loops) system applied to find a best material flow mechanism. Therefore, pull control system is preferred to control production line efficiently. In the production line, the WIP limit has been decided based on mathematical models or expert's decision. However, due to the complexity of the process or increase of the variables, it is difficult to obtain optimal WIP limit. To obtain an optimal WIP limit, GA applied in each material control system. When evaluating the performance of the result, fitness function is used by reflecting WIP parameter. Elite strategy showed better performance than roulette wheel selection when evaluating fitness value. Elite strategy reach to the optimal WIP limit faster than roulette wheel selection and generation time is short. For this reason, this study proposes a fast and reliable method for determining the WIP level by applying genetic algorithm to pull system based production process. This research showed that this method could be applied to a more complex production system.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.236-236
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.237-237
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• 2018

• International Journal of Industrial Entomology and Biomaterials
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• v.14 no.2
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• pp.87-92
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• 2007

Twenty - four elite mulberry germplasm each of indigenous and exotics were studied for their leaf yield and compared with commercial check ($V_1$ and Kosen). Accession MI-0416 and ME-0169 out yielded the check accession in leaf yield/plant. The other few mulberry germplasm were also performed at par with the checks. For quality test and bioassay were conducted with the leaves of selected mulberry germplasm. Among the selected twelve mulberry accessions used for bioassay, MI-0376 and ME-033 performed better than check ($V_1$, Kosen). Other mulberry accessions i.e., MI-0310 and MI-0437 are on par with the check as far as the bioassay is concerned. MI-0376 and ME-0033 out yielded in rearing parameters and qualified for 11 and 10 rearing and related traits. Other mulberry accessions i.e., MI-0310 and MI-0437 were also qualified for eight rearing traits along with check ($V_1$). The mulberry accessions tested after selection from the preliminary characterization seems to be better and equally good in rearing and leaf yield compared to check ($V_1$, Kosen), which provides scope for selection and further evaluation. The selected mulberry accession may be included in crop improvement programme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• Plant Breeding and Biotechnology
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• v.7 no.3
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• pp.272-286
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• 2019

Developing elite hybrid rice varieties is one important objective of rice breeding programs. Several genes related to male sterilities, restores, and pollinators have been identified through map-based gene cloning within natural variations of rice. These identified genes are good targets for introducing genetic traits in molecular breeding. This study was conducted to breed elite hybrid lines with major genes related to hybrid traits and disease/insect resistance in 240 genetic resources and F₁ hybrid combinations of rice. Molecular markers were reset for three major hybrid genes (S5, Rf3, Rf4) and thirteen disease/insect resistant genes (rice bacterial blight resistance genes Xa3, Xa4, xa5, Xa7, xa13, Xa21; blast resistance genes Pita, Pib, Pi5, Pii; brown planthopper resistant genes Bph18(t) and tungro virus resistance gene tsv1). Genotypes were then analyzed using molecular marker-assisted selection (MAS). Biological assay was then performed at the Red River Delta region in Vietnam using eleven F₁ hybrid combinations and two control vatieties. Results showed that nine F₁ hybrid combinations were highly resistant to rice bacterial blight and blast. Finally, eight F₁ hybrid rice varieties with resistance to disease/insect were selected from eleven F₁ hybrid combinations. Their characteristics such as agricultural traits and yields were then investigated. These F₁ hybrid rice varieties developed with major genes related to hybrid traits and disease/insect resistant genes could be useful for hybrid breeding programs to achieve high yield with biotic and abiotic resistance.