• 경영과학
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• 제14권1호
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• pp.77-85
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• 1997

This paper proposes an approach for individual selection in genetic algorithms to improve problem solving efficiency and effectiveness. To investigate the utility of combining simulated annealing with genetic algorithm, two experiment are conducted that compare both the conventional genetic algorithm and suggested approach. Result indicated that suggested approach significantly reduced the required time to find optimal solution in moderate-sized problems under the conditions studied. It is also found that quality of the solutions generated by suggested approach in large- sized problems is greatly improved.

• Management Science and Financial Engineering
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• 제13권1호
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• pp.35-53
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• 2007

We consider a(worst-case) robust optimization version of the Economic Order Quantity(EOQ) model. Order setup costs and inventory carrying costs are assumed to have uncertainty in their values, and the uncertainty description of the two parameters is supposed to be given by an ellipsoidal representation. A genetic algorithm combined with Monte Carlo simulation is proposed to approximate the ellipsoidal representation. The objective function of the model under ellipsoidal uncertainty description is derived, and the resulting problem is solved by another genetic algorithm. Computational test results are presented to show the performance of the proposed method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.612-618
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• 1998

The success of a fuzzy logic control system solving any given problem critically depends on the architecture of th network. Various attempts have been made in optimizing its structure its structure using genetic algorithm automated designs. In a regular genetic algorithm , a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. This paper presents a new approach to structurally optimized designs of a fuzzy model. We use a messy genetic algorithm, whose main characteristics is the variable length of chromosomes. A messy genetic algorithms used to obtain structurally optimized fuzzy models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the exampled of a cart-pole balancing.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.175-178
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• 2003

In recent years, several researchers have presented the extensive research reports on network optimization problems. In our real life applications, many important network problems are typically formulated as a Maximum flow model (MXF) or a Minimum Cost flow model (MCF). In this paper, we propose a Genetic Algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including MXF and MCF models(MXF/MCF).

• 한국안전학회지
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• 제13권4호
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• pp.240-247
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• 1998

This paper proposed a method for solving the nonlinear integer programing problem to get easily the best compromise solution while holding a nonlinear property by using the genetic algorithms. Also, this paper reported that the optimization problem of systems reliability as was solved by using the preposed method, and the numerical comparison experiments between the 0-1 LP/0-1 NP formulations were demonstrated, and from the quantitative evaluation the efficiency of the proposed method was demonstrated.

• 한국정보시스템학회지:정보시스템연구
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• 제17권3호
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• pp.39-58
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• 2008

Mixed Chinese Postman Problem (MCPP) is a practical generalization of the classical Chinese Postman Problem (CPP) and it could be applied in many real world. Although MCPP is useful in terms of reality, MCPP has been proved to be a NP-complete problem. To find optimal solutions efficiently in MCPP, we can reduce searching space to be small effective searching space containing optimal solutions. We propose GENIIS methodology, which is a kind of hybrid algorithm combines the approximate algorithms and genetic algorithm. To get good solutions in the effective searching space, GENIIS uses approximate algorithm and genetic algorithm. This paper validates the usefulness of the proposed approach in a simulation. The results of our paper could be utilized to increase the efficiencies of network and transportation in business.

• 한국수학교육학회지시리즈A:수학교육
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• 제46권3호
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• pp.331-349
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• 2007

This study aims to improve the teaching and learning method on the conic sections. To do that the researcher analyzed the impact of dynamic geometry software on students' problem solving of the conic sections. Students often say, "I have solved this kind of problem and remember hearing the problem solving process of it before." But they often are not able to resolve the question. Previous studies suggest that one of the reasons can be students' tendency to approach the conic sections only using algebra or analytic geometry without the geometric principle. So the researcher conducted instructions based on the geometric and historico-genetic principle on the conic sections using dynamic geometry software. The instructions were intended to find out if the experimental, intuitional, mathematic problem solving is necessary for the deductive process of solving geometric problems. To achieve the purpose of this study, the researcher video taped the instruction process and converted it to digital using the computer. What students' had said and discussed with the teacher during the classes was checked and their behavior was analyzed. That analysis was based on Branford's perspective, which included three different stage of proof; experimental, intuitive, and mathematical. The researcher got the following conclusions from this study. Firstly, students preferred their own manipulation or reconstruction to deductive mathematical explanation or proving of the problem. And they showed tendency to consider it as the mathematical truth when the problem is dealt with by their own manipulation. Secondly, the manipulation environment of dynamic geometry software help students correct their mathematical misconception, which result from their cognitive obstacles, and get correct ones. Thirdly, by using dynamic geometry software the teacher could help reduce the 'zone of proximal development' of Vigotsky.

• 한국산학기술학회논문지
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• 제11권6호
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• pp.2269-2275
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• 2010

유전자 알고리즘은 매개변수와 유전자 연산자 그리고 계획과 같은 다양한 요인들에 의해 영향을 받으며, 전통적인 방법을 통한 문제의 해결은 효율적이지만 전체적으로는 실행 가능성의 문제와 결과의 도출에 걸리는 시간의 문제가 있을 수 있다. 이에 따라 전통적인 유전자 알고리즘은 다양한 방법으로 수정 및 적용되어 질 수 있다. 본 연구는 Travelling Salesman 문제를 해결하기 위해 초기에 정렬된 인자를 사용하여 수정된 유전자 알고리즘을 적용하였다. 본 연구를 통한 접근 방법은 초기 문제의 크기를 줄이며 또한 빠른 복합 수렴을 달성하였다. 또한 제안된 방법은 객체지향 접근을 사용한 시뮬레이터를 통해 테스트 되었고 그 결과는 제안된 방법의 타당성을 입증하였다.

• 한국CDE학회논문집
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• 제2권4호
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• pp.215-221
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• 1997

A new approach in solving design centering problem is presented. Like most stochastic optimization problems, optimal design centering problems have intrinsic difficulties in multivariate intergration of probability density functions. In order to avoid to avoid those difficulties, genetic algorithm and very coarse Monte Carlo simulation are used in this research. The new algorithm performs robustly while producing improved yields. This result implies that the combination of robust optimization methods and approximated simulation schemes would give promising ways for many stochastic optimizations which are inappropriate for mathematical programming.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1025-1028
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• 2000

In this paper, we apply to a genetic algorithm for fuzzy clustering. We propose initialization procedure and genetic operators such as selection, crossover and mutation, which are suitable for solving the problems. To illustrate the effectiveness of the proposed algorithm, we solve the manufacturing cell formation problem and present computational comparisons to generalized Fuzzy c-Means algorithm.