• 산업경영시스템학회지
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• 제28권3호
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• 대한안전경영과학회지
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• 제10권3호
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• pp.217-225
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• 2008

The inventory routing problem (IRP) is an important area of Supply Chain Management. The objective function of IRP is the sum of transportation cost and inventory cost. We propose an Artificial Immune System(AIS) to solve the IRP. AIS is one of natural computing algorithm. An hyper mutation and an vaccine operator are introduced in our research. Computation results show that the hyper mutation is useful to improve the solution quality and the vaccine is useful to reduce the calculation time.

• Journal of applied mathematics & informatics
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• 제30권1_2호
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• pp.183-200
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• 2012

A two-strain epidemic model with an age structure mutation and varying population is studied. By means of the spectrum theory of bounded linear operator in functional analysis, the reproductive numbers according to the strains, which associates with the growth rate ${\lambda}^*$ of total population size are obtained. The asymptotic stability of the steady states are obtained under some sufficient conditions.

• 대한산업공학회지
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• 제25권4호
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• pp.490-499
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• 1999

Vehicle routing problem(VRP) is known to be NP-hard problem, and good heuristic algorithm needs to be developed. To develop a heuristic algorithm for the VRP, this study suggests a parallel genetic algorithm(PGA), which determines each vehicle route in order to minimize the transportation costs. The PGA developed in this study uses two dimensional array chromosomes, which rows represent each vehicle route. The PGA uses new genetic operators. New mutation operator is composed of internal and external operators. internal mutation swaps customer locations within a vehicle routing, and external mutation swaps customer locations between vehicles. Ten problems were solved using this algorithm and showed good results in a relatively short time.

• 정보처리학회논문지D
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• 제16D권4호
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• pp.517-526
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• 2009

동적 UML 다이어그램은 객체 지향 언어로 구현된 프로그램의 복잡한 실행 동작에 대한 표현이 가능하다. 이로 인하여 동적 다이어그램 중, 순서, 상태, 액티비티 다이어그램을 이용하여 테스트 케이스를 추출하고 테스트 하는 방법이 많이 쓰이고 있다. 그러나 테스트 자원과 시간이 제한되어 있을 때 어떤 명세를 이용하여 테스트 케이스를 만드는 것이 더 효율적인지, 또한 어떤 특성이 있는지 알 필요가 있다. 이 논문에서는 ATM 시뮬레이션 프로그램을 세 가지 다이어그램으로 표현하고 이를 이용하여 서로 다른 테스트 케이스를 생성한다. 또한 뮤테이션 테스팅(Mutation Testing)을 실시하여 각 테스트 케이스에 대한 효율을 평가 하였다. 뮤턴트(Mutant) 생성은 절차적 방식과 객체 지향 방식에 의한 뮤테이션 연산자(Mutation Operator)를 구분해서 적용하였으며 뮤클립스(Muclipse)라는 이클립스(Eclipse) 기반의 플러그인 도구를 이용하였다. 생성된 테스트 케이스와 뮤턴트를 이용해서 뮤테이션 점수(Mutation Score)를 측정하고 이를 기반으로 각 테스트 케이스 및 여러 관점에서 테스트 케이스의 효율을 평가하였다. 이런 과정을 통해 테스트 케이스 생성 방식의 선택에 대한 힌트를 얻을 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제2권4호
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of Microbiology and Biotechnology
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• 제12권4호
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• pp.622-627
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• 2002

Diphtheria toxin repressor (DtxR) binds to approximately 30 to 35-bp regions containing an interrupted 9-bp inverted repeat within a 19-bp core sequence. The core sequence is fairly conserved and critical for DtxR binding. The flanking regions that are consisted of 5 to 8 more of nucleotides from the core are also required for DtxR binding. The nucleotides in both flanking regions are A-T rich. To examine whether the A-T nucleotides in both flanking regions from the core have significant roles for DtxR binding, a DNA fragment was constructed based on the diphtheria tox promoter/operator, and DNA fragments with substitution of A and T nucleotides In the flanking regions to G and C were also constructed. To assess the effect of these substitutions on binding of DtxR and repressibility by DtxR, $\beta$-galactosidase activity from lacZ fused to the region was assessed. Gel mobility shift of the region by purified DtxR was also examined. The DNA fragments containing the mutations in the flanking regions still exhibited repression and mobility shift with DtxR. The core segment with the mutation is still, therefore, recognized by DtxR. Nonetheless, the results from the assays indicated that the substitution significantly decreased repression of the operator by DtxR in vivo under high-iron condition and decreased binding of DtxR to the operator. These results suggest that A and T nucleotides fur both flanking regions are preferred for the binding of DtxR.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.609-612
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• 2000

Job-shop scheduling problem(JSSP) is one of the best-known machine scheduling problems and essentially an ordering problem. A new encoding scheme which always give a feasible schedule is presented, by which a schedule directly corresponds to an assigned-operation ordering string. It is initialized with G&T algorithm and improved using the developed genetic operator; APMX or BPMX crossover operator and mutation operator. and the problem of infeasibility in genetic generation is naturally overcome. Within the framework of the newly designed genetic algorithm, the NP-hard classical job-shop scheduling problem can be efficiently solved with high quality. Moreover the optimal solutions of the famous benchmarks, the Fisher and Thompson's 10${\times}$10 and 20${\times}$5 problems, are found.

• 정보과학회 논문지
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• 제41권12호
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• pp.1090-1098
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• 2014

이 연구는 단봉 전역 최적화 성능이 개선된 적응적 코시 분포 차분 진화 알고리즘을 제안한다. 기존 적응적 코시 분포 차분 진화 알고리즘은(ACDE) 개체의 다양성을 보장하여 다봉 전역 최적화 문제에 우수한 "DE/rand/1" 돌연변이 전략을 사용했다. 그러나 이 돌연변이 전략은 수렴 속도가 느려 단봉 전역 최적화 문제에 단점이 있다. 제안 알고리즘은 "DE/rand/1" 돌연변이 전략 대신 수렴 속도가 빠른 "DE/current-to-best/1" 돌연변이 전략을 사용했다. 이때, 개체의 다양성이 부족하여 발생할 수 있는 지역 최적해로의 수렴을 방지하기 위해서 매개변수 초기화 연산이 추가됐다. 매개변수 초기화 연산은 특정세대를 주기로 실행되거나 또는 선택 연산에서 모든 개체가 진화에 실패하는 경우 실행된다. 매개변수 초기화 연산은 각 개체들의 매개변수에 탐험적 특성이 높은 값을 할당하여 넓은 공간을 탐색할 수 있도록 보장한다. 성능 평가 결과, 개선된 적응적 코시 분포 차분 진화 알고리즘이 최신 차분 진화 알고리즘들에 비해 특히, 단봉 전역 최적화 문제에서 성능이 개선됨을 확인했다.

• 정보처리학회논문지B
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• 제9B권5호
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• pp.673-680
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• 2002

이 논문은 진화 프로그래밍과 개선된 역전파 알고리즘을 이용한 에지 검출 방법을 제안한다. 진화 프로그래밍은 알고리즘의 성능저하와 계산비용을 고려하여 교차 연산은 수행하지 않고, 선택연산자와 돌연변이 연산자를 사용한다. 개선된 역전파 알고리즘은 학습단계에서 연결강도를 변화시킬 때 이전학습단계의 연결강도를 보조적으로 활용하는 방법이다. 이 개선된 역전파 알고리즘은 학습률 $\alpha$를 작은값으로 설정하기 때문에 각 학습단계에서의 연결강도 변화량이 기존의 방법에 비해 상대적으로 줄어들게 되어 학습이 느려지는 문제점을 해결하였다. 실험결과 학습시간과 검출률에 있어서 GA-BP(GA : Genetic Algorithm BP : Back-Propagation)를 이용한 방법보다 제안한 EP-MBP(EP : Evolutionary Programming, MBP :Momentum Back-Propagation)를 이용하여 학습시킨 방법이 학습시간의 단축과 효율적인 에지 검출 결과를 얻을 수 있었다.