• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.79-87
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• 2017

The microphysical processes of the numerical weather prediction (NWP) model cover the following : fall speed, accretion, autoconversion, droplet size distribution, etc. However, the microphysical processes and parameters have a significant degree of uncertainty. Parameter estimation was generally used to reduce errors in NWP models associated with uncertainty. In this study, the micro- genetic algorithm and harmony search algorithm were used as an optimization algorithm for estimating parameters. And we estimate parameters of microphysics for the Unified model in the case of precipitation in Korea. The differences which occurred during the optimization process were due to different characteristics of the two algorithms. The micro-genetic algorithm converged to about 1.033 after 440 times. The harmony search algorithm converged to about 1.031 after 60 times. It shows that the harmony search algorithm estimated optimal parameters more quickly than the micro-genetic algorithm. Therefore, if you need to search for the optimal parameter within a faster time in the NWP model optimization problem with large calculation cost, the harmony search algorithm is more suitable.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.5
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• pp.298-311
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• 2003

Iterative Fourier transform algorithm, (IFTA) is tile iterative numerical algorithm for the design of the diffractive optical elements (DOE), by which the phase distribution of a DOE converges on a local optimal solution. The convergence of IFTA depends on several factors 3s initial phase distribution, the structure of the degree of freedom on the observation plane, and the values of internal parameters. In this paper, we analyze tile dependence of the convergence of IFTA on an internal parameter of IFTA, the relaxation parameter, and propose a new hybrid scheme of genetic algorithm and IFTA to obtain more accurate solution.

• Structural Engineering and Mechanics
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• v.32 no.5
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• pp.667-683
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• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.3
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• pp.305-308
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• 2004

The purpose of this study was to estimate the genetic parameters for backfat thickness at shoulder, mid back and loin and days to 90 kg using a derivative-free REML procedure. Data were collected from 6,146 boars and gilts of purebred Durocs, Landraces and Large Whites performance tested at breeding farms of National Agricultural Cooperatives Federation in Korea from 1998 to 2001. Estimated heritability for backfat measurements at shoulder, mid-back and loin and an average of those backfat measurements were 0.14, 0.32, 0.22 and 0.25 in Durocs, 0.34, 0.50, 0.42 and 0.46 in Landraces and 0.33, 0.52, 0.43 and 0.49 in Large Whites. Heritabilities of backfat measurements estimated were hightest in mid-back and lowest at shoulder. Phenotypic variances of backfat measurements estimated were largest at shoulder and smallest at mid-back. Estimated heritabilities for days to 90 kg were 0.37 in Durocs, 0.42 in Landraces and 0.54 in Large Whites. Genetic correlations among backfat measurements at shoulder, mid-back and loin and an average of those backfat measurements estimated were positive and very high. Genetic correlations of days to 90 kg with the backfat measurements estimated were _0.19 ~ _0.30 in Durocs, _0.04 ~ _0.17 in Landraces and _0.10 ~ _0.13 in Large Whites.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.119-126
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• 2002

In this paper, we present a new approach based on an elitist genetic algorithm for the task of aligning the position of a robot gripper using CCD cameras. The vision-based control scheme for the task of aligning the gripper with the desired position is implemented by image information. The relationship between the camera space location and the robot joint coordinates is estimated using a camera-space parameter modal that generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation. To find the joint angles of a robot manipulator for reaching the target position in the image space, we apply an elitist genetic algorithm instead of a nonlinear least square error method. Since GA employs parallel search, it has good performance in solving optimization problems. In order to improve convergence speed, the real coding method and geometry constraint conditions are used. Experiments are carried out to exhibit the effectiveness of vision-based control using an elitist genetic algorithm with a real coding method.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.584-589
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• 2001

This paper presents the dynamic parameter design optimization of a suspension seat system using the genetic algorithm. At first, an equivalent 1-D.O.F. mass-spring-damper model of a suspension seat system was constructed for the purpose of its vibration analysis. Vertical vibration response and transmissibility of the equivalent model due to base excitations, which are defined in the ISO's seat vibration test codes, were computed. Furthermore, seat vibration test, that is ISO's damping test, was carried out in order to investigate the validity of the equivalent suspension seat model. Both analytical and experimental results showed good agreement each other. For the design optimization, the acceleration transmissibility of the suspension seat model was adopted as an object function. A simple genetic algorithm was used to search the optimum values of the design variables, suspension stiffness and damping coefficient. Finally, vibration ride performance test results showed that the optimum suspension parameters gives the lowest vibration transmissibility. Accordingly the genetic algorithm and the equivalent suspension seat modelling can be successfully adopted in the vibration ride quality optimization of a suspension seat system.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.10
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• pp.1379-1383
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• 2006

A total of 6,804 records for Duroc breed were collected from three farms registered at the Korean Animal Improvement Association (KAIA) from 1998 to 2004 of which both records from two ultrasound modes (A and B) were analyzed to estimate the variance components of carcass traits. Three carcass traits backfat thickness (bf), loin eye muscle area (lma) and lean meat percentage (lmp) were measured. These traits were analyzed separately as bf1, lma1 and lmp1 for ultrasound mode A and bf2, lma2 and lmp2 for ultrasound mode B with multiple trait animal model by using MTDFREML (Boldman et al., 1993). All the traits revealed medium heritability values. Estimated heritabilities for bf1, bf2, lma1, lma2, lmp1 and lmp2 were 0.45, 0.39, 0.32, 0.25, 0.28 and 0.39, respectively. Estimated genetic correlations for traits bf1 and bf2, lma1 and lma2, lmp1 and lmp2 were positive but low. Specifically, genetic correlations between bf1 and bf2 was 0.30 while the estimates for lean traits between lma1 and lma2 and between lmp1 and lmp2 were 0.15 and 0.18, respectively. Conversely, high negative genetic correlations existed between bf1 and the lean traits lma2, lmp2. Likewise, the estimated genetic correlations between lma1 and lma2 and lmp1 and lmp2 were low.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.26-31
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• 2018

Objective: This study estimated the genetic parameters for body weight gain and growth curve parameter traits in Korean native chicken (KNC). Methods: A total of 585 $F_1$ chickens were used along with 88 of their $F_0$ birds. Body weights were measured every 2 weeks from hatching to 20 weeks of age to measure weight gain at 2-week intervals. For each individual, a logistic growth curve model was fitted to the longitudinal growth dataset to obtain three growth curve parameters (${\alpha}$, asymptotic final body weight; ${\beta}$, inflection point; and ${\gamma}$, constant scale that was proportional to the overall growth rate). Genetic parameters were estimated based on the linear-mixed model using a restricted maximum likelihood method. Results: Heritability estimates of body weight gain traits were low to high (0.057 to 0.458). Heritability estimates for ${\alpha}$, ${\beta}$, and ${\gamma}$ were $0.211{\pm}0.08$, $0.249{\pm}0.09$, and $0.095{\pm}0.06$, respectively. Both genetic and phenotypic correlations between weight gain traits ranged from -0.527 to 0.993. Genetic and phenotypic correlation between the growth curve parameters and weight gain traits ranged from -0.968 to 0.987. Conclusion: Based on the results of this study population, we suggest that the KNC could be used for selective breeding between 6 and 8 weeks of age to enhance the overall genetic improvement of growth traits. After validation of these results in independent studies, these findings will be useful for further optimization of breeding programs for KNC.