• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes th hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.272-278
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• 2017

In this paper, we consider a single-channel amplitude comparison monopulse system(SCACMS). The monopulse ratio curve(MR-C) of the SCACMS can be controlled by an amplitude difference between sum and different signal, a phase difference and the coefficient of the signal processor. We first propose the SCACMS with multiple variables, and then apply a genetic algorithm to optimize the multiple variables in terms of minimizing a root mean square error. The simulation results show that when three variables of the SCACMS are jointly optimized, the linear region of the MR-C can be extended approximately 187 % compared to that of two variables.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.72-78
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• 2009

This paper considers a multiagent scheduling problem under public information where a machine is shared by multiple agents. Each agent has a local objective among the minimization of total completion time and the minimization of maximum. In this problem, it is assumed that scheduling information is public. Therefore an agent can access to complete information of other agents and pursue efficient schedules in a centralized manner. We propose an enumeration scheme to find Pareto optimal schedules and a multiobjective genetic algorithm as a heuristic approach. Experimental results indicate that the proposed genetic algorithm yields close-to Pareto optimal solution under a variety of experimental conditions.

• CDE review
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• v.21 no.2
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• pp.39-49
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• 2015

This paper presents the single objective design optimization of plate-fin heat sink equipped with fan cooling system using Genetic Algorithm. The proper heat sink and fan model are selected based on the previous studies. And the thermal resistance of heat sinks and fan efficiency during operation are calculated according to specific design parameters. The objective function is combination of thermal resistance and fan efficiency which have been taken to measure the performance of the heat sink. And Decision making procedure is suggested considering life time of semiconductor and Fan Operating cost. And also Analytical Model used for optimization is validated by Fluent, Ansys 13.0 and this model give a quite reasonable and reliable design.

• Journal of the Korea Society for Simulation
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• v.9 no.3
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• pp.91-102
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• 2000

The distribution routing problem is one of the important problems in distribution and supply center management. This research is concerned with an integrated distribution routing problem for multi-supply centers based on improved genetic algorithm and GUI-type programming. In this research, we used a three-step approach; in step 1 a sector clustering model is developed to transfer the multi-supply center problem to single supply center problems which are more easy to be solved, in step 2 we developed a vehicle routing model with time and vehicle capacity constraints and in step 3, we developed a GA-TSP model which can improve the vehicle routing schedules by simulation. For the computational purpose, we developed a GUI-type computer program according to the proposed methods and the sample outputs show that the proposed method is very effective on a set of standard test problems, and it could be potentially useful in solving the distribution routing problems in multi-supply center problem.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.328-335
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• 2011

GPU (Graphics Processing Units) is consists of SIMD (Single Instruction Multiple Data) architecture and provides fast parallel processing. A GA (Genetic Algorithm), which requires large computations, is implemented in GPU using CUDA (Compute Unified Device Architecture). Three kinds of execution models are presented according to different combinations of processing modules in GPU. Comparison experiments between GPU models and CPU are tested for a couple of benchmark problems by variation of population sizes and complexity of problem sizes.

• International Journal of Contents
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• v.16 no.4
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• pp.26-38
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• 2020

Determining the optimal levels of the technical attributes (TAs) of a product to achieve a high level of customer satisfaction is the main activity in the planning process for quality function deployment (QFD). In real applications, the number of customer requirements for developing a single product is quite large, and the number of converted TAs is also high so the size of the house of quality (HoQ) becomes huge. Furthermore, the TA levels are often discrete instead of continuous and the product market can be divided into several market segments corresponding to the number of HoQ, which also unacceptably increases the size of the QFD optimization problem and the time spent on making decisions. This paper proposed a genetic algorithm (GA) solution approach to finding the optimum set of TAs in QFD in the above situation. A numerical example is provided for illustrating the proposed approach. To assess the computational performance of the GA, tests were performed on problems of various sizes using a fractional factorial design.

• Structural Engineering and Mechanics
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• v.80 no.5
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• pp.539-551
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• 2021

With the continuous increase of computational capacity, more and more complex nonlinear elastoplastic constitutive models were developed to study the mechanical behavior of elastoplastic materials. These constitutive models generally contain a large amount of physical and phenomenological parameters, which often require a large amount of computational costs to determine. In this paper, an inverse parameter determination method is proposed to identify the constitutive parameters of elastoplastic materials, with the consideration of both strain rate effect and temperature effect. To carry out an efficient design, a hybrid optimization algorithm that combines the genetic algorithm and the Nelder-Mead simplex algorithm is proposed and developed. The proposed inverse method was employed to determine the parameters for an elasto-viscoplastic constitutive model and Johnson-cook model, which demonstrates the capability of this method in considering strain rate and temperature effect, simultaneously. This hybrid optimization algorithm shows a better accuracy and efficiency than using a single algorithm. Finally, the predictability analysis using partial experimental data is completed to further demonstrate the feasibility of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.9-22
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• 2004

This paper presents a parallel genetic algorithm for the Multi-disk data allocation problem an NP-complete problem. This problem is to find a method to distribute a Binary Cartesian Product File on disk-arrays to maximize parallel disk I/O accesses. A Sequential Genetic Algorithm(SGA), DAGA, has been proposed and showed the superiority to the other proposed methods, but it has been observed that DAGA consumes considerably lengthy simulation time. In this paper, a parallel version of DAGA(ParaDAGA) is proposed. The ParaDAGA is a 2-dimension torus-based Parallel Genetic Algorithm(PGA) and it is based on a distributed population structure. The ParaDAGA has been implemented on the parallel computer simulated on a single processor platform. Through the simulation, we study the impact of varying ParaDAGA parameters and compare the quality of solution derived by ParaDAGA and DAGA. Comparing the quality of solutions, ParaDAGA is superior to DAGA in all cases of configurations in less simulation time.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.175-182
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• 2021

Deadlock is a situation in which two or more processes competing for resources are waiting for the others to finish, and neither ever does. There are two different forms of systems, multi-unit and single-unit resource systems. The difference is the number of instances (or units) of each type of resource. Deadlock problem can be modeled as a constrained combinatorial problem that seeks to find a possible scheduling for the processes through which the system can avoid entering a deadlock state. To solve deadlock problem, several algorithms and techniques have been introduced, but the use of metaheuristics is one of the powerful methods to solve it. Genetic algorithms have been effective in solving many optimization issues, including deadlock Problem. In this paper, an improved parallel framework of the genetic algorithm is introduced and adapted effectively and efficiently to deadlock problem. The proposed modified method is implemented in java and tested on a specific dataset. The experiment shows that proposed approach can produce optimal solutions in terms of burst time and the number of feasible solutions in each advanced generation. Further, the proposed approach enables all types of crossovers to work with high performance.