• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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• pp.55-58
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• 2000

Previous works using a Multi-population Genetic Algorithm have divided chromosome into two components, rule sets and membership functions. However, in this case bad rule sets disturb optimization in good rule sets and membership functions. A new method for a Multi-population Genetic Algorithm suggests three components, good rule sets, bad rule sets, and membership functions. To show the effectiveness of this method, fuzzy controller is applied in a Truck Backing Problem. Results of the computer simulation show good adaptation of the proposed method for a Multi-population Genetic Algorithm.

• Korean Journal of Mathematics
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• 제13권2호
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• pp.161-176
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• 2005

We consider a model of population dynamics whose mortality function is unbounded. We note that the regularity of the solution depends on the growth rate of the mortality near the maximum age. We propose Gauss-Legendre methods along the characteristics to approximate the solution when the solution is smooth enough. It is proven that the scheme is convergent at fourth-order rate in the maximum norm. We also propose discontinuous Galerkin finite element methods to approximate the solution which is not smooth enough. The stability of the method is discussed. Several numerical examples are presented.

• Nonlinear Functional Analysis and Applications
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• 제26권1호
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• pp.83-92
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• 2021

This paper studies approximate solutions for a class of nonlinear diffusion population models. Our methods are to use the fundamental solution of heat equations to construct integral forms of the models and the well-known Banach compression map theorem to prove the existence of positive solutions of integral equations. Non-steady-state local approximate solutions for suitable harvest functions are obtained by utilizing the approximation theorem of multivariate continuous functions.

• 대한건축학회논문집:계획계
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• 제35권4호
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• pp.127-134
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• 2019

Recently, the city development policies are being switched to the policies focusing on recession worldwide. Even though South Korea is also facing the phenomena that small and medium-sized cities are downsized because of a decrease in population and an aging society problem, there is lack of solutions and even they are still planning city development policies based on prospect of high growth. This study is featuring 4 viewpoints depending on shrinking population trend in 7 small and medium sized local cities, which are chosen as downsized cities. It is dealing with population peak, current population, 2030 population, and 2040 population viewpoints. And this study uses downsizing simulation that suggests optimum area that fits population and then shows visual check of the status of the small and medium sized local cities. The area of the small and medium sized local cities are expanded more than needs when they are on population peak point. However, they need only 20% to 25% of current area in 2030 and 2040. The most important thing is realizing the seriousness and facing up to the 'shrinking' phenomena that each small and medium-sized cities suffer. Therefore, this study has significance for presenting the status of small and medium-sized cities' problems in South Korea.

• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.324-333
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• 2013

The purpose of this paper is to improve the efficiency of multi-objective topology optimization using a genetic algorithm (GA) with bar-system representation. We proposed a new GA using an elite initial population obtained from a Solid Isotropic Material with Penalization (SIMP) using a weighted sum method. SIMP with a weighted sum method is one of the most established methods using sensitivity analysis. Although the implementation of the SIMP method is straightforward and computationally effective, it may be difficult to find a complete Pareto-optimal set in a multi-objective optimization problem. In this study, to build a more convergent and diverse global Pareto-optimal set and reduce the GA computational cost, some individuals, with similar topology to the local optimum solution obtained from the SIMP using the weighted sum method, were introduced for the initial population of the GA. The proposed method was applied to a structural topology optimization example and the results of the proposed method were compared with those of the traditional method using standard random initialization for the initial population of the GA.

• Korean Journal of Mathematics
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• 제22권1호
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• pp.29-36
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• 2014

The limiting diffusion of special diploid model can be defined as a discrete generator for the rescaled Markov chain. Choi([2]) defined the operator of projection $S_t$ on limiting diffusion and new measure $dQ=S_tdP$. and showed the martingale property on this operator and measure. Let $P_{\rho}$ be the unique solution of the martingale problem for $\mathcal{L}_0$ starting at ${\rho}$ and ${\pi}_1,{\pi}_2,{\cdots},{\pi}_n$ the projection of $E^n$ on $x_1,x_2,{\cdots},x_n$. In this note we define $$dQ_{\rho}=S_tdP_{\rho}$$ and show that $Q_{\rho}$ solves the martingale problem for $\mathcal{L}_{\pi}$ starting at ${\rho}$.

• 산업공학
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• 제22권3호
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• pp.223-233
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• 2009

In this paper we developed a method for an optimal planning to reorganize Dong offices to enhance the administrative efficiency. First we defined a mathematical model for the optimal planning problem of reorganizing Dong office and developed a genetic algorithm to solve the problem. For the purpose of minimizing standard deviation of population, area and distance among reorganized offices, the constraints such as allocation, distance, area, population, etc. are considered and weights are applied to Dong offices in the downtown and shopping area. The developed algorithm was applied for reorganizing Dong offices in Jongro Gu, Seoul. The results showed that the developed algorithm could be applied for the real world problem. This study may be applied to the optimal decision of reorganization of offices in the similar reorganization or company M&A situations by changing constraints and weights.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.232-235
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• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve the Traveling Salesman Problem(TSP). Because TSP is a well-known combinatorial optimization problem and belongs to a NP-complete problem, there is a huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. In addition, we have the non-schema part to be applied to inversion as well as for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.

• 산업경영시스템학회지
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• 제22권52호
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• pp.275-284
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• 1999

The TTP(TimeTabling Problem) for a university which should be made every term is very difficult problem to schedule because each component must satisfy all fixed constraints. This TTP, therefore, is solved by several heuristic methods. In this paper, applying PPEP(Parallel Population Evolution Programs) for the TTP, we try to find the approximated optimal solution by maintaining independence of each subpopulation and by searching the wide feasible solution. In addition, we present the problem to be appeared when used PMX(Partially mapped crossover)operator for the long chromosomes and then propose GBX (Gene_Based crossover)operator which can complement it. Finally, experimental results are presented comparing EP(Evolution Programs) and PPEP, then GBX which has the random genes and the selected genes on a real TTP.

• 경영과학
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• 제31권3호
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• pp.113-130
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• 2014

Cooperative coevolutionary algorithm (CCEA) has proven to be a very powerful means of solving optimization problems through problem decomposition. CCEA implies the use of several populations, each population having the aim of finding a partial solution for a component of the considered problem. Populations evolve separately and they interact only when individuals are evaluated. Interactions are made to obtain complete solutions by combining partial solutions, or collaborators, from each of the populations. In this respect, we can think of various interaction modes. The goal of this research is to develop a CCEA for a supply chain network design (SCND) problem and identify which interaction mode gives the best performance for this problem. We present general design principle of CCEA for the SCND problem, which require several co-evolving populations. We classify these populations into two groups and classify the collaborator selection scheme into two types, the random-based one and the best fitness-based one. By combining both two groups of population and two types of collaborator selection schemes, we consider four possible interaction modes. We also consider two modes of updating populations, the sequential mode and the parallel mode. Therefore, by combining both four possible interaction modes and two modes of updating populations, we investigate seven possible solution algorithms. Experiments for each of these solution algorithms are conducted on a few test problems. The results show that the mode of the best fitness-based collaborator applied to both groups of populations combined with the sequential update mode outperforms the other modes for all the test problems.