• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• 한국공간구조학회논문집
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• 제8권3호
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• pp.75-82
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• 2008

본 연구에서는 방향벡터(direction vector)를 이용한 지역 탐색법과 유전자 알고리즘을 결합한 새로운 알고리즘인 D-GA를 제안한다. 새로운 개체(individual)를 찾기 위한 방향벡터로는 진화과정 중에 습득되는 정보를 활용하기 위한 학습방향벡터(Loaming direction vector)와 진화와는 무관하게 한 개체의 주변을 탐색하는 랜덤방향벡터(random direction vector) 등 두 가지를 구성하였다. 그리고, 10 부재 트러스 설계 문제에 단순 유전자 알고리즘과 D-GA를 적용하여 최적화를 수행하였고, 그 결과를 비교 검토함으로써 단순 GA에 비하여 D-GA의 정확성 및 효율성이 향상되었음을 확인하였다.

• 한국멀티미디어학회논문지
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• 제4권1호
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• pp.82-90
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• 2001

유전자 알고리즘(GA: Genetic Algorithm)은 다양한 영역에서 NP 문제를 해결하는 방법으로 알려져 있다. GA는 긴 연산 시간을 필요하다는 결점 때문에 최근 GA를 하드웨어로 구현하려는 연구가 주목 받아왔다. 본 논문은 GA의 하드웨어 구현을 위한 전용 원칩 컴퓨터를 제안한다. 제안된 전용 원칩 컴퓨터는16 비트 CPU core와 하드웨어 GA로 구성되어 있다. 기존의 하드웨어 GA는 GA의 처리하는데 있어서 메인 컴퓨터에 의존적이었으나 제안된 전용 원칩 컴퓨터는 메인 컴퓨터에 독립적이다. 또한 기존의 하드웨어 GA는 염색체의 길이가 고정되어 있는 데 비해 제안된 전용 원칩 컴퓨터의 염색체의 길이는 가변이며 16 비트 단위로 Pipeline 처리를 한다. 실험 결과는 제안된 원칩 컴퓨터가 랜덤 비트 동기 회로를 위한 진화 하드웨어 설계에 적용할 수 있다는 것을 보여준다.

• 산업공학
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• 제20권4호
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• pp.504-514
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• 2007

The multicast routing problem lies in the composition of a multicast routing tree including a source node and multiple destinations. There is a trade-off relationship between cost and delay, and the multicast routing problem of optimizing these two conditions at the same time is a difficult problem to solve and it belongs to a multi-objective optimization problem (MOOP). A multi-objective genetic algorithm (MOGA) is efficient to solve MOOP. A micro-genetic algorithm(${\mu}GA$) is a genetic algorithm with a very small population and a reinitialization process, and it is faster than a simple genetic algorithm (SGA). We propose a multi-objective micro-genetic algorithm (MO${\mu}GA$) that combines a MOGA and a ${\mu}GA$ to find optimal solutions (Pareto optimal solutions) of multicast routing problems. Computational results of a MO${\mu}GA$ show fast convergence and give better solutions for the same amount of computation than a MOGA.

• 한국공작기계학회논문집
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• 제12권3호
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• pp.82-88
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• 2003

SGA(Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, ${\mu}GA$(Micro-Genetic Algorithm) has recently been developed. In this study, ${\mu}GA$ which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of ${\mu}GA$ were compared with those of SGA. Solutions of ${\mu}GA$ for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that ${\mu}GA$ is a suitable and very efficient optimization algorithm for structural design.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.9-14
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• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• 한국경영과학회지
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• 제21권1호
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• pp.147-161
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• 1996

Generally the Multiprocessor Scheduling (MPS) problem is difficult to solve because of the precedence of the tasks, and it takes a lot of time to obtain its optimal solution. Though Genetic Algorithm (GA) does not guarantee the optimal solution, it is practical and effective to solve the MPS problem in a reasonable time. The algorithm developed in this research consists of a improved GA and GP/MISF (Critical Path/Most Immediate Successors First). An efficient genetic operator is derived to make GA more efficient. It runs parallel CP/MISF with GA to complement the faults of GA. The solution by the developed algorithm is compared with that of CP/MISF, and the better is taken as a final solution. As a result of comparative analysis by using numerical examples, although this algorithm does not guarantee the optimal solution, it can obtain an approximate solution that is much closer to the optimal solution than the existing GA's.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1995년도 추계학술대회발표논문집; 서울대학교, 서울; 30 Sep. 1995
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• pp.220-229
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• 1995

Generally the Multiprocessor Scheduling(MPS) problem is difficult to solve because of the precedence of the tasks, and it takes a lot of time to obtain its optimal solution. Though Genetic Algorithm(GA) does not guarantee the optimal solution, it is practical and effective to solve the MPS problem in a reasonable time. The algorithm developed in this research consists of a improved GA and CP/MISF(Critical Path/Most Immediate Successors First). A new genetic operator is derived to make GA more efficient. It runs parallel CP/MISF with Ga to complement the faults of GA. The solution by the developed algorithm is compared with that of CP/MISF, and the better is taken as a final solution. As a result of comparative analysis by using numerical examples, although this algorithm does not guarantee the optimal solution, it can obtain an approximate solution that is much closer to the optimal solution than the existing GA's.

• 산업경영시스템학회지
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• 제22권53호
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• pp.37-44
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• 1999

The problem to be studied here is the minimization of the total travel distance and the number of vehicles used for delivering goods to customers. Vehicle routes must also satisfy a variety of constraints such as fixed vehicle capacity, allowed operating time. Genetic algorithm to solve the VRPTW with heterogeneous fleet is presented. The chromosome of the proposed GA in this study has the 3-dimension. We propose GA that has the cubic-chromosome for VRPTW with heterogeneous fleet. The newly suggested ‘Cubic-GA (or 3-D GA)’ in this paper means the 2-D GA with GLS(Genetic Local Search) algorithms and is quite flexible. To evaluate the performance of the algorithm, we apply it to the Solomon's VRPTW instances. It produces a set of good routes and the reasonable number of vehicles.

• 한국산업정보학회논문지
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• 제24권5호
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• pp.53-64
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• 2019

본 연구에서는 재사용 가능한 제품을 대상으로 순방향물류(Forward logistics)에서 부터 역방향물류(Reverse logistics)에 이르기까지 전체 물류비용과 수요와 회수에 따른 제조업자에서의 재고관리, 재사용을 위한 과정에서 발생하는 청소공정비용 및 폐기비용을 고려한 재사용 네트워크 모델(Reusable network model)을 제안한다. 제안 모델의 유효성을 검증하기 위하여 최적화 기법 중 하나인 유전자 알고리즘(Genetic algorithm: GA)을 이용한다. 파라미터가 해(Solution)에 미치는 영향을 알아보기 위해서 세 가지 파라미터 조건에서 우선 순위형 GA(Priority-based GA: priGA)와, 각 세대(Generation)마다 파라미터가 조정되는 개량형 하이브리드 GA(Modified hybrid genetic algorithm: mhGA)를 사이즈가 다른 4가지 예제에 적용하여 시뮬레이션을 실시한다.