• Asian-Australasian Journal of Animal Sciences
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• v.17 no.2
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• pp.153-158
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• 2004

Data containing 14,188 lactation and reproductive records of Korean Holstein cows at first parity distributed across 3,734 herd-year-season groups were analyzed to get genetic (co)variance estimates for milk yield, fat yield, calving ease, and days open. Milk and Fat yields were adjusted to 305 d. Heritabilities and genetic correlations were estimated in two different animal models on which were included direct genetic effects (Model 1) and direct+maternal genetic effects (Model 2) using REML algorithms. Milk and fat yields were affected by age at first calving as linear and quadratic. Heritability estimates of direct effects were 0.25 for milk yield, 0.17 for fat yield, 0.03 for calving ease and 0.03 for days open in Model 2. These estimates for maternal effects were 0.05, 0.08, 0.04 and less than 0.01 for each corresponding trait. Milk productions at first lactation were to show genetically favorable correlation with calving ease and days open for direct genetic effects (-0.24 - -0.11). Moreover, calving ease was correlated with days open of 0.30 for direct genetic effects. Correlations between direct and maternal effects for each trait were negatively correlated (-0.63 - -0.32). This study suggested that maternal additive genetic variance would be not ignorable for genetic evaluation of milk production as well as reproductive traits such as calving ease and days open at first parity. Furthermore, difficult calving would genetically influence the next conception.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.302-309
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• 2005

We have studied a model and application that evolves the behavioral knowledge of a virtual robot. The knowledge is represented in classification rules and a neural network, and is learned by a genetic algorithm. The model consists of a virtual robot with behavior knowledge, an environment that it moves in, and an evolution performer that includes a genetic algorithm. We have also applied our model to an environment where the robots gather food into a nest. When comparing our model with the conventional method on various test cases, our model showed superior overall learning.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.3
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• pp.515-532
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• 1997

When balancing mixed model assembly lines (MMALs), workload smoothness should be considered on the model-by-model basis as well as on the station-by-station basis. This is because although station-by-station assignments may provide the equality of workload to workers, it causes the utilization of assembly lines to be inefficient due to the model sequences. This paper presents a genetic algorithm to improve the workload smoothness on both the station-by-station and the model-by-model basis in balancing MMALs. Proposed is a function by which the two kinds of workloads smoothness can be evaluated according to the various preferences of line managers. To enhance the capability of searching good solutions, our genetic algorithm puts emphasis on the utilization of problem-specific information and heuristics in the design of representation scheme and genetic operators. Experimental results show that our algorithm can provide better solutions than existing heuristics. In particular, our algorithm is outstanding on the problems with a larger number of stations or a larger number of tasks.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.1
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• pp.7-12
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• 2004

Real time ultrasonic measurements for 13th rib fat thickness (LBF), longissimus muscle area (LEMA) and marbling score (LMS) of live animal at pre-harvest and subsequent carcass measurements for fat thickness (BF), longissimus muscle area (EMA), marbling score (MS) as well as body weight of live animal, carcass weight (CW), dressing percentage (DP), and total merit index (TMI) on 755 Korean beef steers were analyzed to estimate genetic parameters. Data were analyzed using multivariate animal models with an EM-REML algorithm. Models included fixed effects for year-season of birth, location of birth, test station, age of dam, linear and quadratic covariates for age or body weight at slaughter and random animal and residual effects. The heritability estimates for LEMA, LBF and LMS on RTU scans were 0.17, 0.41 and 0.55 in the age-adjusted model (Model 1) and 0.20, 0.52 and 0.55 in the weight-adjusted model (Model 2), respectively. The Heritability estimates for subsequent traits on carcass measures were 0.20, 0.38 and 0.54 in Model 1 and 0.23, 0.46 and 0.55 in Model 2, respectively. Genetic correlation estimate between LEMA and EMA was 0.81 and 0.79 in Model 1 and Model 2, respectively. Genetic correlation estimate between LBF and BF were high as 0.97 in Model 1 and 0.98 in Model 2. Real time ultrasonic marbling score were highly genetically correlated to carcass MS of 0.89 in Model 1 and 0.92 in Model 2. These results indicate that RTU scans would be alterative to carcass measurement for genetic evaluation of meat quality in a designed progeny-testing program in Korean beef cattle.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.2
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• pp.160-165
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• 2007

Dispersion parameters for the number of piglets born alive were estimated using a repeatability and random regression model. Six sow breeds/lines were included in the analysis: Swedish Landrace, Large White and both crossbred lines between them, German Landrace and their cross with Large White. Fixed part of the model included sow genotype, mating season as month-year interaction, parity and weaning to conception interval as class effects. The age at farrowing was modelled as a quadratic regression nested within parity. The previous lactation length was fitted as a linear regression. Random regressions for parity on Legendre polynomials were included for direct additive genetic, permanent environmental, and common litter environmental effects. Orthogonal Legendre polynomials from the linear to the cubic power were fitted. In the repeatability model estimate of heritability was 0.07, permanent environmental effect as ratio was 0.04, and common litter environmental effect as ratio was 0.01. Estimates of genetic parameters with the random regression model were generally higher than in the repeatability model, except for the common litter environmental effect. Estimates of heritability ranged from 0.06 to 0.10. Permanent environmental effect as a ratio increased along a trajectory from 0.03 to 0.11. Magnitudes of common litter effect were small (around 0.01). The eigenvalues of covariance functions showed that between 7 and 8 % of genetic variability was explained by individual genetic curves of sows. This proportion was mainly covered by linear and quadratic coefficients. Results suggest that the random regression model could be used for genetic analysis of litter size.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.167-175
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• 2006

It has long been recognized that cancer is a genetic disease. To find this measures of genetic instability, stain cells with chromosome specific probes using chromosome in-situ hybridization technique is adopted. Even though in-situ hybridization technique is powerful, truncation of nuclei often results in under-representation of chromosome copies in slides due to the sectioning of tissue blocks. Because of this problem we suggest three different methods to analyze the cervical cancer data set. We observe that genetic instability is an increasing function of histology and our suggested model is the best in detecting genetic instability of tumorigenesis processes.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.481-488
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• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1056-1060
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• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.