• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.63-82
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• 2007

In this paper, we propose an adaptive hybrid genetic algorithm (ahGA) approach for effectively solving multistage-based scheduling problems in flexible manufacturing system (FMS) environment. The proposed ahGA uses a neighborhood search technique for local search and an adaptive scheme for regulation of GA parameters in order to improve the solution of FMS scheduling problem and to enhance the performance of genetic search process, respectively. In numerical experiment, we present two types of multistage-based scheduling problems to compare the performances of the proposed ahGA with conventional competing algorithms. Experimental results show that the proposed ahGA outperforms the conventional algorithms.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.295-308
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• 2015

We have experienced a substantial improvement in and cost-drop for genotyping that enables genetic epidemiological studies with large-scale genetic data. Genome-wide association studies have identified more than ten thousand causal variants. Many statistical methods based on linear mixed models have been developed for various goals such as estimating heritability and identifying disease susceptibility locus. Empirical results also repeatedly stress the importance of linear mixed models. Therefore, we review the statistical methods related with to linear mixed models and illustrate the meaning of their estimates.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.425-432
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• 2009

In this study, the ability of neural network in modeling and predicting of the unsteady aerodynamic force coefficients of 2D airfoil with the data obtained from Euler CFD code has been confirmed. Neural network models are constructed based on supervised training process using Levenberg-Marquardt algorithm, combining this into genetic algorithm, hybrid genetic algorithm and the efficiency of the two cases are analyzed and compared. It is shown that hybrid-genetic algorithm is more efficient for neural network of complex system and the predicted properties of the unsteady aerodynamic force coefficients of 2D airfoil by the neural network models are confirmed to be similar to that of the numerical results and verified as suitable representing reduced models.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.6
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• pp.7-13
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• 2002

This paper introduced a new hybrid genetic algorithm, verified its performance, and applied it to the optimization of blade design and pitch control for 30kW pitch-controlled variable-speed horizontal-axis wind turbine system to determine the optimum blade chord and twist distributions that maximize the energy production for a given Weibull wind distribution and the optimum blade pitch angles that maintain constant power output.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.79-89
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• 2017

The Optimization of a Closed-Loop Supply Chain (CLSC) Model Using an Adaptive Hybrid Genetic Algorithm (AHGA) Approach is Considered in this Paper. With Forward and Reverse Logistics as an Integrated Logistics Concept, The CLSC Model is Consisted of Various Facilities Such as Part Supplier, Product Manufacturer, Collection Center, Recovery Center, etc. A Mathematical Model and the AHGA Approach are Used for Representing and Implementing the CLSC Model, Respectively. Several Conventional Approaches Including the AHGA Approach are Used for Comparing their Performances in Numerical Experiment.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.10a
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• pp.74-74
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• 1999

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.443-445
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• 1999

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.503-518
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• 2007

Normalization transforms one parent genotype to be consistent with the other before crossover. In this paper, we explain how normalization alleviates the difficulties caused by non-synonymously redundant encodings in genetic algorithms. We define the encodings with maximally non-synonymous property and prove that the encodings induce uncorrelated search spaces. Extensive experiments for a number of problems show that normalization transforms the uncorrelated search spaces to correlated ones and leads to significant improvement in performance.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.170-177
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• 2014

Genetic Algorithms are robust search and optimization techniques but have some problems such as premature convergence and convergence to local extremum. As population diversity converges to low value, the search ability decreases and converges to local extremum but population diversity converges to high value, then the search ability increases and converges to global optimum or genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we propose the genetic operators with the hybrid function of the average Hamming distance and the fitness value to maintain the diversity of the GA's population for escaping from the premature convergence. Results of simulation studies verified the effects of the mutation operator for maintaining diversity and the other operators for improving convergence properties as well as the feasibility of using proposed genetic operators on convergence properties to avoid premature convergence and convergence to local extremum.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.159-169
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• 2020

A cold chain logistics (CCL) model for chilled food (-1℃ to 8℃) distributed in China was developed in this study. The CCL model consists of a distribution center (DC) and distribution target points (DT). The objective function of the CCL model is to minimize the total distribution routes of all distributors. To find the optimal result of the objective function, the hybrid genetic algorithm (HGA) approach is proposed. The HGA approach was constructed by combining the improved K-means and genetic algorithm (GA) approaches. In the case study, three scenarios were considered for the CCL model based on the distribution routes and the available distance, and they were solved using the proposed HGA approach. Analysis results showed that the distribution costs and mileage were reduced by approximately 19%, 20% and 16% when the proposed HGA approach was used.