• 한국지능시스템학회논문지
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• 제17권1호
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• pp.86-93
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• 2007

A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.

• Journal of Genetic Medicine
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• 제15권2호
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• pp.55-63
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• 2018

Clinical and genetic heterogeneity in association with overlapping spectrum is characteristic in pediatric neuromuscular disorders, which makes confirmative diagnosis difficult and time consuming. Considering evolution of molecular genetic diagnosis and resultant upcoming genetically modifiable therapeutic options, rapid and cost-effective genetic testing should be applied in conjunction with existing diagnostic methods of clinical examinations, laboratory tests, electrophysiologic studies and pathologic studies. Earlier correct diagnosis would enable better clinical management for these patients in addition to new genetic drug options and genetic counseling.

• Journal of Genetic Medicine
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• 제5권2호
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• pp.89-93
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• 2008

"Personalized medicine," the goal of which is to provide better clinical care by applying patient's own genomic information to their health care is a global challenge for the $21^{st}$ century "genomic era." This is especially true in Korea, where provisions for clinical genetic services are inadequate for the existing demand, let alone future demands. Genomics-based knowledge and tools make it possible to approach each patient as a unique biological individual, which has led to a paradigm-shift in medical practice, giving it more of a predictive focus as compared with current treatment oriented approach. With recent advancements in genomics, many genetic tests, such as susceptibility genetic tests, have been developed for both rare single gene diseases and more common multifactorial diseases. Indeed, genetic tests for presymtomatic individuals and genetic tests for drug response have become widely available, and personalized medicine will face the challenge of assisting patients who use such tests to make appropriate and wise use of genetic risk assessment. A major challenge of genomic medicine lies in understanding and communicating disease risk in order to facilitate and support patients and their families in making informed decisions. Establishment of a health care system with provisions for genetic counseling as an integral part of health care service, in addition to genomic literacy of health care providers, is vital to meet this growing challenge. Realization of the promise of personalized medicine in the era of genomics for improvement of health care is dependent on further development of next generation sequencing technology and affordable sequencing test costs. Also necessary will be policy development concerning the ethical, legal and social issues of genomic medicine and an educated and ready medical community with clinical practice guidelines for genetic counseling and genetic testing.

• Animal Bioscience
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• 제36권12호
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• pp.1806-1820
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• 2023

Objective: The present study was aimed primarily for estimating various genetic parameters (heritability, genetic correlations) of reproduction (age at first calving [AFC], first service period [FSP]); production (first lactation milk, solid-not fat, and fat yield) and lifetime traits (lifetime milk yield, productive life [PL], herd life [HL]) in Tharparkar cattle to check the association of reproduction traits with lifetime traits through two different methods (Frequentist and Bayesian) for comparative purpose. Methods: Animal breeding data of Tharparkar cattle (n = 964) collected from Livestock farm unit of ICAR-NDRI Karnal for the period 1990 through 2019 were analyzed using a Frequentist least squares maximum likelihood method (LSML; Harvey, 1990) and a multi-trait Bayesian-Gibbs sampler approach (MTGSAM) for genetic correlations estimation of all the traits. Estimated breeding values of sires was obtained by BLUP and Bayesian analysis for the production traits. Results: Heritability estimates of most of the traits were medium to high with the LSML (0.20±0.44 to 0.49±0.71) and Bayesian approach (0.24±0.009 to 0.61±0.017), respectively. However, more reliable estimates were obtained using the Bayesian technique. A higher heritability estimate was obtained for AFC (0.61±0.017) followed by first lactation fat yield, first lactation solid-not fat yield, FSP, first lactation milk yield (FLMY), PL (0.60±0.013, 0.60±0.006, 0.57±0.024, 0.57±0.020, 0.42±0.025); while a lower estimate for HL (0.38±0.034) by MTGSAM approach. Genetic and phenotypic correlations were negative for AFC-PL, AFC-HL, FSP-PL, and FSP-HL (-0.59±0.19, -0.59±0.24, -0.38±0.101 and -0.34±0.076) by the multi-trait Bayesian analysis. Conclusion: Breed and traits of economic importance are important for selection decisions to ensure genetic gain in cattle breeding programs. Favourable genetic and phenotypic correlations of AFC with production and lifetime traits compared to that of FSP indicated better scope of AFC for indirect selection of life-time traits at an early age. This also indicated that the present Tharparkar cattle herd had sufficient genetic diversity through the selection of AFC for the improvement of first lactation production and lifetime traits.

• Genomics & Informatics
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• 제17권1호
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• pp.4.1-4.7
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• 2019

In this paper, we propose a window-based mechanism visualization approach as an alternative way to measure the seriousness of the difference among data-insights extracted from a composite biodata point. The approach is based on two components: undirected graph and Mosaab-metric space. The significant application of this approach is to visualize the segmented genome of a virus. We use Influenza and Ebola viruses as examples to demonstrate the robustness of this approach and to conduct comparisons. This approach can provide researchers with deep insights about information structures extracted from a segmented genome as a composite biodata point, and consequently, to capture the segmented genetic variations and diversity (variants) in composite data points.

• 한국CDE학회논문집
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• 제9권3호
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• pp.183-191
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• 2004

This paper deals with smooth curve fitting of data corrupt by noise. Most research efforts have been concentrated on employing the smoothness penalty function with the estimation of its optimal parameter in order to avoid the 'overfilling and underfitting' dilemma in noisy data fitting problems. Our approach, called DBSF(Differentiation-Based Smooth Fitting), is different from the above-mentioned method. The main idea is that optimal functions approximately estimating the derivative of noisy curve data are generated first using genetic programming, and then their integral values are evaluated and used to recover the original curve form. To show the effectiveness of this approach, DBSP is demonstrated by presenting two illustrative examples and the application of estimating the principal dimensions of bulk cargo ships in the conceptual design stage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1679-1691
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• 2016

Successive interference cancellation (SIC) is an effective means of multi-packet reception to combat interference at the physical layer. We investigate the joint optimization issue of channel access and power control for capacity maximization in SIC-enabled wireless networks. We propose a new interference model to characterize the sequential detection nature of SIC. Afterward, we formulize the joint optimization problem, prove it to be a nondeterministic polynomial-time-hard problem, and propose a novel approximation approach based on the genetic algorithm (GA). Finally, we discuss the design and parameter setting of the GA approach and validate its performance through extensive simulations.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(II)
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• pp.829-836
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• 2002

The paper presents a multi-objective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer's desire of providing excess power at nodes and designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and applicable to water distribution systems is presented. The present model is applied to two example problems, which were widely reported. Pipe failure analysis carried out on some of the solutions obtained revealed that the network resilience based approach gave better results in terms of network reliability.

• Industrial Engineering and Management Systems
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• 제11권2호
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• pp.155-164
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• 2012

As the global market becomes more competitive, manufacturing industries face relentless pressure caused by a growing tendency of greater varieties of products, shorter manufacturing cycles and more sophisticated customer requirements. Efficient and effective supplier selection and order allocation decisions are, therefore, important decisions for a manufacturer to ensure stable material flows in a highly competitive supply chain, in particular, when customers are willing to accept products with less desirable product attributes (e.g., color, delivery date) for economic reasons. This paper attempts to solve optimally the challenging problem of supplier selection and order allocation, taking into consideration the customer flexibility for a manufacturer producing multi-products to satisfy the customers' demands in a multi period planning horizon. A new mixed integer programming model is developed to describe the behavior of the supply chain. The objective is to maximize the manufacturer's total profit subject to various operating constraints of the supply chain. Due to the complexity and non-deterministic polynomial-time (NP)-hard nature of the problem, an improved genetic approach is proposed to solve the problem optimally. This approach differs from a canonical genetic algorithm in three aspects: a new selection method to reduce the chance of premature convergence and two problem-specific repair heuristics to guarantee feasibility of the solutions. The results of applying the proposed approach to solve a set of randomly generated test problems clearly demonstrate its excellent performance. When compared with applying the canonical genetic algorithm to locate optimal solutions, the average improvement in the solution quality amounts to as high as ten percent.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 봄 학술발표회 논문집
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• pp.125-132
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• 2000

This study presents the application of genetic algorithms(GA) to the back analysis of tunnels. GA based on the theory of natural evolution, and have been evaluated very effective for their robust performances, particularly for optimizing structure problems. In the back analysis method, the selection of initial value and uncertainty of field measurements influence significantly on the analysis result. GA can improve this problems through a probabilistic approach. Besides, this technique have two other advantages over the back analysis. One is that it is not significantly affected by the form of problems. Another one is that it can consider two known parameter simultaneously. The propriety of this study is verified as the comparison in the same condition of the back analysis(Gens et al, 1987). In this study, it was performed to estimated the geotechnical parameters in the case of weak rock mass at the Kyung Bu Express railway tunnel. GA have been shown for effective application to a geotechnical engineering.