• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.961-966
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• 2008

Direct and maternal genetic parameters for production traits in 1,642 pigs and maternal genetic correlations among production (1,642 pigs) and feed efficiency (380 boars) traits were estimated in 7 generations of a Duroc population. Traits studied were daily gain (DG), intramuscular fat (IMF), loineye area (LEA), backfat thickness (BF), daily feed intake (FI), feed conversion ratio (FCR) and residual feed intake (RFI). The RFI was calculated as the difference between actual and predicted feed intake. The predicted feed intake was estimated by adjusting the initial test weight, DG and BF. Data for production traits were analyzed using four alternative animal models (including direct, direct+maternal permanent environmental, or direct+maternal genetic+maternal permanent environmental effects). Direct heritability estimates from the model including direct and all maternal effects were $0.41{\pm}0.04$ for DG, $0.27{\pm}0.04$ for IMF, $0.52{\pm}0.06$ for LEA and $0.64{\pm}0.04$ for BF. Estimated maternal heritabilities ranged from $0.04{\pm}0.04$ to $0.15{\pm}0.05$ for production traits. Antagonistic relationships were observed between direct and maternal genetic effects ($r_{am}$) for LEA (-0.21). Maternal genetic correlations of feed efficiency traits with FI ($r_g$ of FI with FCR and RFI were $0.73{\pm}0.06$ and $0.90{\pm}0.05$, respectively) and LEA (rg of LEA with FCR and RFI were $-0.48{\pm}0.05$ to $-0.61{\pm}0.05$, respectively) were favorable. The estimated moderate genetic correlations between direct and maternal genetic effects for IMF and LEA indicated that maternal effects has an important role in these traits, and should be accounted for in the genetic evaluation system.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.5
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• pp.636-642
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• 2018

Objective: The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods: A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results: All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from $0.78{\pm}0.01$ to $0.82{\pm}0.03$, between the first and second parities, from $0.73{\pm}0.03$ to $0.8{\pm}0.04$ between the first and third parities, and from $0.82{\pm}0.02$ to $0.84{\pm}0.04$ between the second and third parities. Conclusion: These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.54-64
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• 2011

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

• Clinical and Experimental Pediatrics
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• v.58 no.4
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• pp.117-122
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• 2015

Global developmental delay (GDD) is a relatively common early-onset chronic neurological condition, which may have prenatal, perinatal, postnatal, or undetermined causes. Family history, physical and neurological examinations, and detailed history of environmental risk factors might suggest a specific disease. However, diagnostic laboratory tests, brain imaging, and other evidence-based evaluations are necessary in most cases to elucidate the causes. Diagnosis of GDD has recently improved because of remarkable advances in genetic technology, but this is an exhaustive and expensive evaluation that may not lead to therapeutic benefits in the majority of GDD patients. Inborn metabolic errors are one of the main targets for the treatment of GDD, although only a small proportion of GDD patients have this type of error. Nevertheless, diagnosis is often challenging because the phenotypes of many genetic or metabolic diseases often overlap, and their clinical spectra are much broader than currently known. Appropriate and cost-effective strategies including up-to-date information for the early identification of the "treatable" causes of GDD are needed for the development of well-timed therapeutic applications with the potential to improve neurodevelopmental outcomes.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.74-74
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• 2017

Wheat is one of the world's most important crop species. Recently, abnormal climate changes directly influence on the significant reduction of wheat productivity in the world. This threaten allow breeders to find new genetic resources. Wheat is one of the widely grown crops in the world. Individual cultivars / germplasm is adapted in that region where the climate is unique to each other. Therefore, introducing new genetic resources which was good in one place may better perform in another region. In this study, we evaluated germplasm in Korean environment and measured numerous agro-morphological characteristics. Information that are provided by the National Agrobiodiversity Center (Jeonju, Korea) and National Plant Germplasm System (Aberdeen, USA) were included in the analysis. Cluster analysis was performed using the unweight pair-group method of averages. The results of PCA indicated principal discriminatory characteristics of wheat landraces and varieties. Significant differences indicated high variability among the quantitative traits. Cluster analysis results showed that the groups were divided by geological climate condition. The preliminary evaluation of germplasms in Korean environment would help to develop wheat cultivars via providing useful genetic traits that are resided in alien germplasms.

• Computers and Concrete
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• v.11 no.6
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• pp.541-569
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• 2013

There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.862-864
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• 1996

This paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic algorithm(GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transferring problem to minimize over load generated load point outage in each sub-section. This kind of the approach is one of the most difficult procedure which becomes a combination problems. A new approach using GA Was developed for this problem. We proposed a tree search algorithm which satisfied the tree constraint. GA is general purpose optimization techniques based on principles inspired from the biological evolution such as natural selection, genetic recombination and survival of the fittest Test results for the model system with 24 nodes and 29 branches are reported in the paper.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.04a
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• pp.69-69
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• 2019

Pepper (Capsicum spp.) is one of the main quality features of this crop because of its sense of pungency, which is due to the presence of capsaicinoids. This compound is synthesized as a secondary metabolite and found only in the placental tissue of spicy fruit (Suzuki et al., 1980). Stewart et al. (2005) concluded that Pun1 encodes for the acyltransferase AT3 and they demonstrated its involvement in capsaicinoids metabolism. It was analyzed that the capsaicinoids content and PUN1 genotype in pepper genetic resources which were selected with excellent phenotype in field evaluation. The number of pepper genetic resources analyzed was 135, and species were C. annuum, C. baccatum, C. chinense, C. frutescens. The content of capsaicinoid ranged from 0 mg/100g to 828 mg/100g. The content of 0 mg/100g was the sweet pepper type, the highest content is IT 158530, the capsaicinoid content of which was 828 mg/100g and species was C. annuum. PUN1 gene analysis showed 117 pungent, 5 hetero, and 13 non-pungent. PUN1 analysis showed that 5 out of 13 non-pungent accessions were detected with low levels of capsaicinoid.

• Computers and Concrete
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• v.30 no.3
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• pp.225-236
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• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.58 no.4
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• pp.359-366
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• 2022

In this study, the load fluctuation of the main engine is considered to be a disturbance for the jacket coolant temperature control system of the low-speed two-stroke main diesel engine on the ships. A nonlinear PID temperature control system with satisfactory disturbance rejection performance was designed by rapidly transmitting the load change value to the controller for following the reference set value. The feed-forwarded load fluctuation is considered the set points of the dual loop control system to be changed. Real-coded genetic algorithms were used as an optimization tool to tune the gains for the nonlinear PID controller. ITAE was used as an evaluation function for optimization. For the evaluation function, the engine jacket coolant outlet temperature was considered. As a result of simulating the proposed cascade nonlinear PID control system, it was confirmed that the disturbance caused by the load fluctuation was eliminated with satisfactory performance and that the changed set value was followed.