• Asian-Australasian Journal of Animal Sciences
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• v.19 no.12
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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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.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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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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• 1999.10a
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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.

• Proceedings of the KIEE Conference
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• 2000.07d
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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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• 2001.10a
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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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• v.66 no.2
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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.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.25-32
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• 2015

The Genetic algorithm is a search algorithm using evaluation, genetic operator, natural selection to populational solution iteratively. The convergence and divergence characteristic of genetic algorithm are affected by selection strategy, generation replacement method, genetic operator when genetic algorithm is designed. This paper proposes fast convergence genetic algorithm for time-limited robot path planning. In urgent situation, genetic algorithm for robot path planning does not have enough time for computation, resulting in quality degradation of found path. Proposed genetic algorithm uses fast converging selection strategy and generation replacement method. Proposed genetic algorithm also uses not only traditional crossover and mutation operator but additional genetic operator for shortening the distance of found path. In this way, proposed genetic algorithm find reasonable path in time-limited situation.

• Journal of Animal Science and Technology
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• v.65 no.5
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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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• v.54 no.5
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• pp.337-340
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• 2012

The genetic connectedness between herds is an essential requirement to make robust across-herd estimation of the breeding values of the animals. In this study, genetic connectedness between herds was evaluated by a connectedness rating method. A total of 24,971 records of days to 90 kg (D90KG) of the pigs on performance testing programs collected from six herds (labeled from 'A' to 'F') of Duroc breed along with pedigree information comprising 456,697 families were used. Results showed that a total of eight boars were used for semen exchange programs among participant farms. Herds 'A' through 'E' were found strongly connected among them. But 'F' herd was genetically connected strongly only with 'A' herd. The highest average connectedness rating was 91.7% between 'A' herd and 'C' herd. The lowest average connectedness rating was 65.1% between 'D' and 'F'. The concept of a single genetic group comprising six Duroc herds studied is meaningful due to high connectedness rates among them. Therefore, with this high genetic ties between participant Duroc farms, the more accurate genetic evaluation would be possible.