• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.233-236
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• 2007

A new evolution method for strong exploration and strong exploitation termed queen-bee and mutant-bee evolution is proposed based on the previous queen-bee evolution [1]. Even though the queen-bee evolution has shown very good performances, two parameters for strong mutation are added to the genetic algorithms. This makes the application of genetic algorithms with queen-bee evolution difficult because the values of the two parameters are empirically decided by a trial-and-error method without a systematic method. The queen-bee and mutant-bee evolution has no this problem because it does not need additional parameters for strong mutation. Experimental results with typical problems showed that the queen-bee and mutant-bee evolution produced nearly similar results to the best ones of queen-bee evolution even though it didn't need to select proper values of additional parameters.

• Communications for Statistical Applications and Methods
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• v.11 no.2
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• pp.355-368
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• 2004

In order to use Kriging, one has to estimate three parameters(nugget, sill and range) of semivariogram, which shows the relationship in the given two sites. A visual fit of the semivariogram parameters to a few standard models is widely used. But, it does not give the suitable results and not provide the automated process of Kriging. The gradient based nonlinear least squares is another choices to estimate three parameters, but it has some problems such as initial value problem. In this paper, we suggest the genetic algorithm as a compatible alternative method to solve the above mentioned problem. Finally, we estimate three parameters of semivariogram of rain-fall by adapting the genetic algorithm, compute Kriging estimate and conclude its effectiveness and compatibility.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.417-422
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• 2007

A new evolution method termed queen-bee and mutant-bee evolution is based on the previous queen-bee evolution [1]. Even though the queen-bee evolution has shown very good performances, two parameters for strong mutation are added to the genetic algorithms. This makes the application of genetic algorithms with queen-bee evolution difficult because the values of the two parameters are empirically decided by a trial-and-error method without a systematic method. The queen- bee and mutant-bee evolution has no this problem because it does not need additional parameters for strong mutation. Experimental results with typical problems showed that the queen-bee and mutant-bee evolution produced nearly similar results to the best ones of queen-bee evolution even though it didn't need to select proper values of additional parameters.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.1
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• pp.13-18
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• 2006

Two different animal models, which differ in whether or not taking maternal genetic effect into account, for estimating genetic parameters of cashmere weight, live body weight, cashmere thickness, staple length, fiber diameter, and fiber length in Inner Mongolia White Cashmere Goat were compared via likelihood ratio test. The results indicate that maternal genetic effect has significant influence on live body weight and cashmere thickness, but no significant influence on the other traits. Using models suitable for each trait, both genetic parameters and trends were analyzed with the MTDFREML program. Heritability estimates from single trait models for cashmere weight, live body weight, cashmere thickness, staple length, fiber diameter and fiber length were found to be 0.30, 0.07, 0.21, 0.29, 0.28 and 0.21, respectively. Genetic correlation estimates from two-trait models between live body weight and all other traits (-0.06~0.07) was negligible, as were those between fiber diameter and all other traits (-0.01~0.03) except cashmere thickness (0.19). Cashmere weight and staple length had moderate to low genetic correlations with other traits (-0.24~0.39 and -0.24~0.34, respectively) except for live body weight and fiber diameter. Cashmere thickness had a strong genetic correlation with fiber length (0.81), and low genetic correlation with other traits (0.19~0.34) except live body weight. Genetic trend analysis suggests that selection for cashmere weight was very effective, which has led to the slow genetic progress of cashmere thickness and fiber length due to their genetic correlations with cashmere weight. The selection for live body weight was not effective, which was consistent with its low inheritability.

• Journal of the Korean Statistical Society
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• v.4 no.2
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• pp.139-151
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• 1975

Individual effect of genes controlling quantitative traits can not ordinarily be distinguised from one another. Consequently, it is not possible to determine the mode of inheritance for single genes. By studying their combined effectsin segregating generations, however, one can gain some insight into their behavior and can make statistical inferences about their average gene action. The investigation reported herein was to extend genetic variance components and variance and covariance analyses, special attention was given to the genetic statistics from which least square estimators of genetic parameters are obtained.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.331-334
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• 1996

It is a well-known problem that the adaptive control has a poor transient response. In order to improve this problem, the scheme that model-reference adaptive control (MRAC) uses the genetic algorithm (GA) in the search for parameters is proposed. Use genetic algorithm (GA) in the searching for controller's parameters set and conventional gradient method for fine tuning. And show the reduction of the oscillations in transient response comparing with the conventional MRAC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1551-1558
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• 2021

Beacon signals used in indoor positioning system are reflected and distorted, resulting in noise signals. KF(Kalman Filter) has been widely used to remove this noise. In order to apply the KF, optimization process considering the signal type, signal strength, and environmental elements of each product is required. In this paper, we propose a solution to the optimization problem of KF Parameters using GA(Genetic Algorithm) in BLE(Bluetooth Low Energy) Beacon-based indoor positioning system. After optimizing KF Parameters by applying the proposed technique with a certain distance between Beacon and receiver, we compared the estimated distance passed through KF with the unfiltered distance. The proposed technique is expected to reduce the time required and improve accuracy of KF Parameters optimization in an indoor positioning system based on RSSI (Received Signal Strength Indication).

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.10
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• pp.1388-1393
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• 2015

Genetic parameters of Berkshire pigs for reproduction, carcass and meat quality traits were estimated using the records from a breeding farm in Korea. For reproduction traits, 2,457 records of the total number of piglets born (TNB) and the number of piglets born alive (NBA) from 781 sows and 53 sires were used. For two carcass traits which are carcass weight (CW) and backfat thickness (BF) and for 10 meat quality traits which are pH value after 45 minutes (pH45m), pH value after 24 hours (pH24h), lightness in meat color (LMC), redness in meat color (RMC), yellowness in meat color (YMC), moisture holding capacity (MHC), drip loss (DL), cooking loss (CL), fat content (FC), and shear force value (SH), 1,942 pig records were used to estimate genetic parameters. The genetic parameters for each trait were estimated using VCE program with animal model. Heritability estimates for reproduction traits TNB and NBA were 0.07 and 0.06, respectively, for carcass traits CW and BF were 0.37 and 0.57, respectively and for meat traits pH45m, pH24h, LMC, RMC, YMC, MHC, DL, CL, FC, and SH were 0.48, 0.15, 0.19, 0.36, 0.28, 0.21, 0.33, 0.45, 0.43, and 0.39, respectively. The estimate for genetic correlation coefficient between CW and BF was 0.27. The Genetic correlation between pH24h and meat color traits were in the range of -0.51 to -0.33 and between pH24h and DL and SH were -0.41 and -0.32, respectively. The estimates for genetic correlation coefficients between reproductive and meat quality traits were very low or zero. However, the estimates for genetic correlation coefficients between reproductive traits and drip and cooking loss were in the range of 0.12 to 0.17 and -0.14 to -0.12, respectively. As the estimated heritability of meat quality traits showed medium to high heritability, these traits may be applicable for the genetic improvement by continuous measurement. However, since some of the meat quality traits showed negative genetic correlations with carcass traits, an appropriate breeding scheme is required that carefully considers the complexity of genetic parameters and applicability of data.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.9
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• pp.1231-1236
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• 2005

Genetic parameters for first lactation milk production based on test day (TD) records of 56319 Iranian Holstein cows from 655 herds that first calved between 1991 and 2001 were estimated with restricted maximum likelihood method under an Animal model. Traits analyzed were milk yield and milk fat percentage. Heritability for TD records were highest in second half of the lactation, ranging from 0.11 to 0.19 for milk yield and 0.038 to 0.094 for milk fat percentage respectively. Estimates for lactation records for these traits were 0.24 and 0.26 respectively. Genetic correlations between individual TD records were high for consecutive TD records (>0.9) and decreased as the interval between tests increased. Estimates of genetic correlations of TD yield with corresponding lactation yield were highest (0.78 to 0.86) for mid-lactation (TD3 to TD8). Phenotypic correlations were lower than corresponding genetic correlations, but both followed the same pattern. For milk fat percentage no clear pattern was found. Results of this study suggested that TD yields especially in mid-lactation may be used for genetic evaluation instead of 305-day yield.

• Journal of Animal Science and Technology
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• v.52 no.3
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• pp.187-190
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• 2010

The objective of this study was to estimate genetic parameters for total number of born (TNB), number of born alive (NBA) and teat numbers (TN) of Landrace and Yorkshire breeds in Korean swine population using multiple trait animal model procedures. Total numbers of 4,653 records for teat numbers and 8,907 records for TNB and NBA collected from 2004 to 2008 on imported breeding pigs and their litter size records were used in this study. To find the appropriate model for estimation of genetic parameters (heritabilities and genetic correlations), five statistical models (two models for reproductive traits, two models for teat numbers, one model for combining these traits) considering only direct additive genetic effects, including maternal effects were used and Akaike information criteria (AIC) of each two models for reproductive traits and teat trait were compared. The means and standard deviations of TNB, NBA, and TN were $11.52{\pm}3.34$, $10.55{\pm}2.96$ and $14.30{\pm}0.83$, respectively. Estimated heritabilities for TNB and NBA traits using the model which considered only additive genetic effect were low (0.06 and 0.05, respectively). However, estimated heritabilities considering maternal genetic effects were a little bit higher than that of the model considering only additive genetic effect (0.09 for TNB and NBA, respectively). Estimated heritability for TN using the model which considered only additive genetic effect was 0.40. However, estimated heritability of direct genetic effects from a model considering maternal genetic effect was high (0.60). All results of AIC statistics, the models considering maternal effect was more appropriate than the models considering only additive genetic effect. Genetic correlations of direct additive genetic effect between litter size (TNB, NBA) and teat numbers were low (-0.18 and -0.14, respectively). However, genetic correlations of maternal effect between litter size (TNB, NBA) and teat numbers were a little bit higher than those of direct additive genetic effect (0.08 and 0.16, respectively).