• Asian-Australasian Journal of Animal Sciences
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• 제19권12호
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• pp.1685-1690
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• 2006

Kenkatha cattle, a draft purpose breed, which can survive in a harsh environment on low quality forage, was explored genetically exploiting FAO-suggested microsatellite markers. The microsatellite genotypes were derived by means of the polymerase chain reaction (PCR) followed by electrophoretic separation in agarose gels. The PCR amplicons were visualized by silver staining. The allelic as well as genotypic frequencies, heterozygosities and gene diversity were estimated using standard techniques. A total of 125 alleles was distinguished by the 21 microsatellite markers investigated. All the microsatellites were highly polymorphic with mean allelic number of 5.95${\pm}$1.9 (ranging from 3-10 per locus). The observed heterozygosity in the population ranged between 0.250 and 0.826 with a mean of 0.540${\pm}$0.171, signifying considerable genetic variation. Bottleneck was examined assuming all three mutation models which showed that the population has not experienced bottleneck in recent past. The population displayed a heterozygote deficit of 21.4%. The study suggests that the breed needs to be conserved by providing purebred animals in the breeding tract.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.425-428
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• 2004

Flexible assembly line (FAL) is a production system that assembles various parts in unidirectional flow line with many constraints and manufacturing flexibilities. In this research we deal with a FAL balancing problem with the objective of minimizing the maximum workload allocated to the stations. However, almost all the existing researches do not appropriately consider various constraints due to the problem complexity. Therefore, this thesis addresses a balancing problem of FAL with many constraints and manufacturing flexibilities, unlike the previous researches. To solve this problem we use a genetic algorithm (GA). To apply GA to FAL, we suggest a genetic representation suitable for FAL balancing and devise evaluation method for individual's fitness and genetic operators specific to the problem, including efficient repair method for preserving solution feasibility. The experimental results are reported.

• 품질경영학회지
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• 제32권4호
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.292-297
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• 1999

Evolving in artificial agent is an extremely difficult problem, but on the other hand, a challenging task. At present the studies mainly centered on single agent learning problem. In our case, we use simulated soccer to investigate multi-agent cooperative learning. Consider the fundamental differences in learning mechanism, existing reinforcement learning algorithms can be roughly classified into two types-that based on evaluation functions and that of searching policy space directly. Genetic Programming developed from Genetic Algorithms is one of the most well known approaches belonging to the latter. In this paper, we give detailed algorithm description as well as data construction that are necessary for learning single agent strategies at first. In following step moreover, we will extend developed methods into multiple robot domains. game. We investigate and contrast two different methods-simple team learning and sub-group loaming and conclude the paper with some experimental results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3010-3012
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• 2000

In this paper. we suggest a vector fitness strategy for obstacle avoidance of autonomous mobile robot with genetic algorithm. Ordinary genetic algorithms provide not such a viable solution for autonomous running in a variant environment. because of the difficulty in fitness evaluation in real time. We show that the suggested method is efficient for the problem of autonomous mobile robot. Its control function evolves to adapt the varying environment. The experiment is done using the real mobile robot 'Khepera' with which we use a tournament genetic algorithm model with the Vectored Fitness Genetic Strategy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.40.6-40
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• 2001

Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...

• Journal of Animal Science and Technology
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• 제66권2호
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• pp.366-373
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• 2024

This study aims to estimate the connectedness rating (CR) of Korean swine breeding herds. Using 104,380 performance and 83,200 reproduction records from three swine breeds (Yorkshire, Landrace and Duroc), the CR was estimated for two traits: average daily gain (ADG) and number born alive (NBA) in eight breeding herds in the Republic of Korea (hereafter, Korea). The average CR for ADG in the Yorkshire breed ranges from 1.32% to 28.5% depending on the farm. The average CR for NBA in the Yorkshire herd ranges from 0% to 12.79%. A total of 60% of Yorkshire and Duroc herds satisfied the preconditions suggested for genetic evaluation among the herds. The precondition for the genetic evaluation of CR for ADG, as a productive trait, was higher than 3% and that of NBA, as a reproductive trait, was higher than 1.5%. The ADG in the Yorkshire herds showed the highest average CR. However, the average CR of ADG in the Landrace herds was lower than the criterion of the precondition. The prediction error variance of the difference (PEVD) was employed to assess the validation of the CR, as PEVDs exhibit fluctuations that are coupled with the CR across the herds. A certain degree of connectedness is essential to estimate breeding value comparisons between pig herds. This study suggests that it is possible to evaluate the genetic performance together for ADG and NBA in the Yorkshire herds since the preconditions were satisfied for these four herds. It is also possible to perform a joint genetic analysis of the ADG records of all Duroc herds since the preconditions were also satisfied. This study provides new insight into understanding the genetic connectedness of Korean pig breeding herds. CR could be utilized to accelerate the genetic progress of Korean pig breeding herds.

• 한국컴퓨터정보학회논문지
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• 제20권4호
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• pp.25-32
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• 2015

유전자 알고리즘은 초기 해 집합을 대상으로 해 집합의 평가와 유전자 연산자의 적용, 자연 선택 등의 과정을 반복하여 최적 해를 찾는 탐색 알고리즘이다. 유전자 알고리즘을 설계할 때 사용한 선택 전략, 세대교체 방법, 유전자 연산자 등은 유전자 알고리즘의 탐색 효율성에 영향을 준다. 본 논문에서는 시간 제약이 있는 상황에서의 로봇 경로 탐색을 위해 기존의 유전자 알고리즘보다 빠르게 수렴하는 유전자 알고리즘을 제안한다. 로봇 경로 탐색 시 긴급한 상황에서 유전자 알고리즘은 연산을 위한 충분한 시간을 확보하지 못 하게 되고, 이는 최종적으로 찾아낸 경로의 질을 떨어뜨린다. 제안하는 알고리즘은 빠른 수렴을 위한 선택 전략, 세대교체 방법을 사용하였으며, 유전자 연산자로는 전통적인 교차, 돌연변이 외에 경로의 길이를 줄이기 위한 단축 연산자를 추가로 사용하였다. 이를 통해 제안하는 알고리즘은 적은 세대 수에도 빠르게 짧은 경로를 찾아낸다.

• Journal of Animal Science and Technology
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• 제65권5호
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• pp.912-921
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• 2023

Genetic diversity analysis is crucial for maintaining and managing genetic resources. Several studies have examined the genetic diversity of Korean domestic chicken (KDC) populations using microsatellite markers, but it is difficult to capture the characteristics of the whole genome in this manner. Hence, this study analyzed the genetic diversity of several KDC populations using high-density single nucleotide polymorphism (SNP) genotype data. We examined 935 birds from 21 KDC populations, including indigenous and adapted Korean native chicken (KNC), Hyunin and Jeju KDC, and Hanhyup commercial KDC populations. A total of 212,420 SNPs of 21 KDC populations were used for calculating genetic distances and fixation index, and for ADMIXTURE analysis. As a result of the analysis, the indigenous KNC groups were genetically closer and more fixed than the other groups. Furthermore, Hyunin and Jeju KDC were similar to the indigenous KNC. In comparison, adapted KNC and Hanhyup KDC populations derived from the same original species were genetically close to each other, but had different genetic structures from the others. In conclusion, this study suggests that continuous evaluation and management are required to prevent a loss of genetic diversity in each group. Basic genetic information is provided that can be used to improve breeds quickly by utilizing the various characteristics of native chickens.

• Journal of Animal Science and Technology
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• 제54권5호
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• pp.337-340
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• 2012

돼지에서 농장간 유전적 연결성은 다른 농장간 추정 육종가의 비교를 위해 매우 중요한 지표이다. 본 연구에서는 유전적 연결고리가 존재하는 6개의 두록 농장에서 수집된 24,971두의 90 kg 도달 일령에 대한 검정자료 및 456,697두의 혈통정보를 이용하였으며, 농장간 연결성은 연결율 수식에 의해 계산하였다. 분석 결과, 두록 농장간 교류를 위하여 총 8두에서 생산된 정액이 사용된 것으로 나타났다. A 농장은 8두의 종돈을 통하여 나머지 5개 농장과 모두 종돈 교류가 이루어졌으며, B-E 농장의 경우 F 농장을 제외한 나머지 4개 농장과의 종돈 교류가 있었으며, F 농장은 오직 A농장과 혈연적인 연결이 존재하는 것으로 나타났다. 그러나 F 농장은 A농장과의 연결을 통하여 나머지 다른 농장과 연결성을 갖게 되어 두록 6개 농장간 연결성이 존재하는 것으로 나타났다. 또한 농장간 연결율이 가장 높은 값은 A농장과 C 농장간의 91.7%로 6개의 두록 농장 중 농장간 연결율이 가장 높았으며, 반대로 D농장과 F 농장에서 65.1%로 가장 낮은 연결율을 나타냈다. 국내 6개 두록 종돈장간 연결율은 65% 이상으로 고도의 유전적 연결성이 존재하는 것으로 나타나 단일 집단의 개념에서 유전평가가 가능하며, 이를 이용하여 농장내외 개체간 상대적 비교를 통해 우수 종축을 선발할 수 있을 것으로 사료된다.