• Asian-Australasian Journal of Animal Sciences
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• v.19 no.12
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• pp.1696-1701
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• 2006

A random regression model was applied for the first time for the analysis of test day records and to study the genetic persistency of first lactation milk yield of Indian Murrah buffaloes. Wilmink's Function was chosen to describe the shape of lactation curves. Heritabilities of test day milk yield varied from 0.33 to 0.58 in different test days. The highest heritability was found in the initial test day ($5^{th}$ day) milk yield. Genetic correlations among test day milk yields were higher in the initial test day milk yield and decreased when the test day interval was increased. The magnitude of genetic correlations between test day and 305 day milk yield varied from 0.25 to 0.99. The genetic persistencies of first lactation milk yield were estimated based on daily breeding values using two methods. $P_1$ is the genetic persistency estimated as a summation of the deviation of estimated daily breeding value on days to attain peak yield from each day after days to attain peak yield to different lactation days. $P_2$ is the genetic persistency estimated as the additional genetic yield (gained or lost) from days to attain peak yield to estimated breeding value on different lactation days relative to an average buffalo having the same yield on days to attain peak yield. The mean genetic persistency on 90, 120, 180, 240, 278 and 305 days in milk was estimated as -4.23, -21.67, -101.67, -229.57, -330.06 and -388.64, respectively by $P_1$, whereas by $P_2$ on same days in milk were estimated as -3.96 (-0.32 kg), -23.94 (-0.87 kg), -112.81 (-1.96 kg), -245.83 (-2.81 kg), -350.04 (-3.28 kg) and -407.58 (-3.40 kg) respectively. Higher magnitude of rank correlations indicated that the ranking of buffaloes based on their genetic persistency in both methods were similar for evaluation of genetic persistency of buffaloes. Based on the estimated range of genetic persistency three types of genetic persistency were identified. Genetic correlations among genetic persistency in different days in milk and between genetic persistencies on the same day in milk were very high. The genetic correlations between genetic persistency for different days in milk and estimated breeding value for 305 DIM was increased from 90 DIM to 180 DIM, and highest around 240 DIM which indicates a minimum of 240 days as an optimum first lactation length might be required for genetic evaluation of Indian Murrah buffaloes.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.4
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• pp.441-444
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• 1998

The negative direct-maternal genetic correlation $(r_{dm})$ for weaning weight is inflated when data are analyzed with model ignoring sire-by-year interactions (SY). An analytical study investigating the consequences of ignoring SY was undertaken. The inflation of negative correlation could be due to a functional relationship of design matrices for additive direct and maternal genetic effects to that for sire effects within which SY effects were nested. It was proven that the maternal genetic variance was inflated by the amount of reduction for sire variance; the direct genetic variance was inflated by four times the change for maternal genetic variance; and the direct-maternal genetic covariance was deflated by twice the change for maternal genetic variance. The findings were agreed to the results in previous studies.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.376-378
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• 2004

Genetic algorithm was motivated by biological evaluation and has been applied to many industrial applications as a powerful tool for mathematical optimizations. In this paper, a new genetic optimization algorithm is proposed. The proposed method is based on Mendel's law, especially dominance and recessive property. Homologous chromosomes are introduced to implement dominance and recessive property compared with the standard genetic algorithm. Because of this property of suggested genetic algorithm, homologous chromosomes looks like the chromosomes for the standard genetic algorithm, so we can use most of existing genetic operations with little effort. This suggested method searches the larger solution area with the less probability of the premature convergence than the standard genetic algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.135-142
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• 2011

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

• Korean Management Science Review
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• v.20 no.1
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• pp.51-64
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• 2003

The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

• Proceedings of the Korean Society of Plant Pathology Conference
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• 2004.10a
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• pp.46-46
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• 2004

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• Asian-Australasian Journal of Animal Sciences
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• v.19 no.7
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• pp.938-942
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• 2006

Genetic diversity of Magra - a lustrous carpet wool breed of India, was investigated by means of 25 ovine microsatellite markers proposed by the Food and Agriculture Organization and the International Society for Animal Genetics (FAO-ISAG). All used microsatellites amplified well and exhibited polymorphisms. A wide range of genetic variability was observed as allele number from 3 (BM6506, OarCP20) to 10 (CSSM31), observed heterozygosity from 0.200 (BM6506) to 0.947 (OarHH35), expected heterozygosity from 0.368 (CSSM47) to 0.864 (BM1314) and Polymorphism Information Content (PIC) from 0.347 (CSSM47) to 0.849 (BM1314). This supported the utility of these microsatellite loci in the measurement of genetic diversity indices in Indian sheep too. Various average genetic variability measures viz., allele diversity (5.7), observed heterozygosity (0.597), expected heterozygosity (0.694) and mean PIC (0.648) values showed high genetic variability despite accumulated inbreeding as reflected by the high average inbreeding coefficient ($F_{IS}=0.159$) due to the unequal sex ratio of the breeding animals.

• Proceedings of the Korea Environmental Mutagen Society Conference
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• 2001.10b
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• pp.191-192
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• 2001

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.171-178
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• 2004

This study suggests a general paradigm enhancing genetic mutability. Mutability among heterogeneous members in a genetic population has been a major problem in application of genetic programming to diverse business problems. This suggested paradigm is implemented to developing new methods from existing methods. Within the evolutionary approach taken to designing new methods, a general representation scheme of the genetic programming framework, called a kernel, is introduced. The kernel is derived from the literature of algorithms and heuristics for combinatorial optimization problems. The commonality and differences among these methods have been identified and again combined by following the genetic inheritance merging them. The kernel was tested for selected methods in combinatorial optimization. It not only duplicates the methods in the literature, it also confirms that each of the possible solutions from the genetic mutation is in a valid form, a running program. This evolutionary method suggests diverse hybrid methods in the form of complete programs through evolutionary processes. It finally summarizes its findings from genetic simulation with insight.

• Korean Journal of Biological Psychiatry
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• v.3 no.1
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• pp.14-21
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• 1996

Molecular genetic approaches contribute to the understanding of the underlying genetic mechanism for schizophrenia. Currently genetic evidence rests on molecular genetic methods. However, the result are contradictory and somewhat confusing due to genetic heterogeneity, incomplete penetrance, misspecification of genetic model. It is expected that molecular genetics could provide key answers to the genetic cause of schizophrenia. The purpose of this article is to call attention of the readers to heterogeneity, linkage, association, basic molecular genetic methods and genetic markers and to the need far further research. It is the author's hope thai continuous research on the molecular genetics con provide clinicians with better understanding of the schizophrenia.