• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.9-12
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• 2000

Genetic algorithm is a searching method which based on the law of the survival of the fittest. Multi-population Genetic Algorithm is a modified form of Genetic Algorithm, which was devised for covering the defect of general genetic algorithm. The core of multi-population genetic algorithm is said to be the migration schemes. The fitness-based migration scheme and the random migration scheme are currently used. In this paper, a new migration scheme, ‘the migration scheme between groups’, is suggested, and compared to the general two migration schemes.

• Animal Bioscience
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• v.34 no.4
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• pp.516-524
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• 2021

Objective: The genetic evaluation of Santa Inês sheep was performed for resistance to gastrointestinal nematode infection (RGNI) and body size using different relationship matrices to assess the efficiency of including genomic information in the analyses. Methods: There were 1,637 animals in the pedigree and 500, 980, and 980 records of RGNI, thoracic depth (TD), and rump height (RH), respectively. The genomic data consisted of 42,748 SNPs and 388 samples genotyped with the OvineSNP50 BeadChip. The (co)variance components were estimated in single- and multi-trait analyses using the numerator relationship matrix (A) and the hybrid matrix H, which blends A with the genomic relationship matrix (G). The BLUP and single-step genomic BLUP methods were used. The accuracies of estimated breeding values and Spearman rank correlation were also used to assess the feasibility of incorporating genomic information in the analyses. Results: The heritability estimates ranged from 0.11±0.07, for TD (in single-trait analysis using the A matrix), to 0.38±0.08, for RH (using the H matrix in multi-trait analysis). The estimates of genetic correlation ranged from -0.65±0.31 to 0.59±0.19, using A, and from -0.42±0.30 to 0.57±0.16 using H. The gains in accuracy of estimated breeding values ranged from 2.22% to 75.00% with the inclusion of genomic information in the analyses. Conclusion: The inclusion of genomic information will benefit the direct selection for the traits in this study, especially RGNI and TD. More information is necessary to improve the understanding on the genetic relationship between resistance to nematode infection and body size in Santa Inês sheep. The genetic evaluation for the evaluated traits was more efficient when genomic information was included in the analyses.

• IE interfaces
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• v.21 no.4
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• pp.444-455
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• 2008

In the present manufacturing environment, the appropriate decision making strategy has a significance and it should count on the fast-changing demand of customers. This research derives the optimal levels of the decision variables affecting the inventory related performance in multi-stage supply chain by using simulation and genetic algorithm. Simulation model helps analyze the customer service level of the supply chain computationally and the genetic algorithm searches the optimal solutions by interaction with the simulation model. Our experiments show that the integration approach of the genetic algorithm with a simulation model is effective in finding the solutions that achieve predefined target service levels.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.322-325
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• 2003

In this paper, we propose n new fuzzy model identification of time series system using wavelet transform and genetic DNA code. Generally, it is well known that the DNA coding method is more diverse in the knowledge expression and better in the optimization performance than the genetic algorithm (GA) because it can encode more plentiful genetic information based on the biological DNA. The proposed method can construct a fuzzy model using the wavelet transform, in which the coefficients are identified by the DNA coding method. Thus, we can effectively get the fuzzy model of the nonlinear system by using the advantages of both wavelet transform and DNA coding method. In order to demonstrate the superiority of the proposed method, it is compared with modeling method using the conventional GA.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.3
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• pp.164-175
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• 2010

In this study, we developed a genetic algorithm to find a near optimal solution of concurrent spare parts selection for the operational time period with limited information of weapon systems purchased from overseas. Through the analysis of time profiles related with system operations, we first define the optimization goal which maintains the expected system operating rate under the budget restrictions, and the number of failures and the lead time for each spare part are used to calculate the estimated total down time of the system. The genetic algorithm for CSP selection shows that the objective function minimizes the estimated total down time of systems with satisfying the restrictions. The method provided by this study can be applied to the generalized model of CSP selection for the systems purchased from overseas without provision of their full structure and adequate information.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.10
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• pp.1491-1500
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• 2019

Objective: European pigs have been imported to improve the economically important traits of Thai pigs by crossbreeding and was finally completely replaced. Currently Thai indigenous pigs are particularly kept in a small population. Therefore, indigenous pigs risk losing their genetic diversity and identity. Thus, this study was conducted to perform large-scale genetic diversity and phylogenetic analyses on the many pig breeds available in Thailand. Methods: Genetic diversity and phylogenetics analyses of 222 pigs belonging to Thai native pigs (TNP), Thai wild boars (TWB), European commercial pigs, commercial crossbred pigs, and Chinese indigenous pigs were investigated by genotyping using 26 microsatellite markers. Results: The results showed that Thai pig populations had a high genetic diversity with mean total and effective ($N_e$) number of alleles of 14.59 and 3.71, respectively, and expected heterozygosity ($H_e$) across loci (0.710). The polymorphic information content per locus ranged between 0.651 and 0.914 leading to an average value above all loci of 0.789, and private alleles were found in six populations. The higher $H_e$ compared to observed heterozygosity ($H_o$) in TNP, TWB, and the commercial pigs indicated some inbreeding within a population. The Nei's genetic distance, mean $F_{ST}$ estimates, neighbour-joining tree of populations and individual, as well as multidimensional analysis indicated close genetic relationship between Thai indigenous pigs and some Chinese pigs, and they are distinctly different from European pigs. Conclusion: Our study reveals a close genetic relationship between TNP and Chinese pigs. The genetic introgression from European breeds is found in some TNP populations, and signs of genetic erosion are shown. Private alleles found in this study should be taken into consideration for the breeding program. The genetic information from this study will be a benefit for both conservation and utilization of Thai pig genetic resources.

• Ocean and Polar Research
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• v.31 no.4
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• pp.319-326
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• 2009

Analysis of four microsatellite markers from Mud Crab Scylla serrata revealed that there is high level of genetic diversity within this species. Genetic diversity of S. serrata was calculated using allele diversity, observed heterozygosity, expected heterozygosity (Het-exp), polymorphic information content, gene differentiation and Nei's $D_{A}$ distance. Mean polymorphic information content value was 0.797, which reflected high level of polymorphism across the loci of S. serrata. The Palau population has the highest genetic diversity (Het-exp=0.871), while the Kosrae population has the lowest genetic diversity (Hetexp=0.806). However, the geographical genetic distance among S. serrata populations from Yab, Chuuk, Pohnpei, Kosrae, and Palau were low (0.2009${\sim}$0.3350). These results suggest that despite their wide distribution, S. serrata are no different in geographical genetic diversity within the five sampled locations.

• The Journal of Information Systems
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• v.12 no.2
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• pp.1-20
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• 2003

This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

• Journal of Sasang Constitutional Medicine
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• v.32 no.2
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• pp.10-21
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• 2020

Objectives Genome-wide association studies(GWAS) is a useful method to identify genetic associations for various phenotypes. The purpose of this study was to develop predictive models for Sasang constitution types using genetic factors. Methods The genotypes of the 1,999 subjects was performed using Axiom Precision Medicine Research Array (PMRA) by Life Technologies. All participants were prescribed Sasang Constitution-specific herbal remedies for the treatment, and showed improvement of original symptoms as confirmed by Korean medicine doctor. The genotypes were imputed by using the IMPUTE program. Association analysis was conducted using a logistic regression model to discover Single Nucleotide Polymorphism (SNP), adjusting for age, sex, and BMI. Results & Conclusions We developed models to predict Korean medicine constitution types using identified genectic factors and sex, age, BMI using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). Each maximum Area Under the Curve (AUC) of Teaeum, Soeum, Soyang is 0.894, 0.868, 0.767, respectively. Each AUC of the models increased by 6~17% more than that of models except for genetic factors. By developing the predictive models, we confirmed usefulness of genetic factors related with types. It demonstrates a mechanism for more accurate prediction through genetic factors related with type.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1553-1557
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• 2004

A real-time processing system for embedded hardware genetic algorithm is suggested. In order to operate basic module of genetic algorithm in parallel, such as selection, crossover, mutation and evaluation, dual processors based architecture is implemented. The system consists of two Xscale processors and two FPGA with evolvable hardware, which enables to process genetic algorithm efficiently by distributing the computational load of hardware genetic algorithm to each processors equally. The hardware genetic algorithm runs on Linux OS and the resulted chromosome is executed on evolvable hardware in FPGA. Furthermore, the suggested architecture can be extended easily for a couple of connected processors in serial, making it accelerate to compute a real-time hardware genetic algorithm. To investigate the effect of proposed approach, performance comparisons is experimented for an typical computation of genetic algorithm.