• 제어로봇시스템학회논문지
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• 제3권1호
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• pp.52-60
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• 1997

In genetic algorithms(GA), a crossover is performed only at one or two places of a chromosome, and the fixed probabilities of crossover and mutation have been used during the entire generation. A GA with dynamic mutation is known to be superior to GAs with static mutation in performance, but so far no efficient dynamic mutation method has been presented. Accordingly in this paper, a GA is proposed to perform a uniform crossover based on the nucleotide(NU) concept, where DNA and RNA consist of NUs and also a concrete way to vary the probabilities of crossover and mutation dynamically for every generation is proposed. The efficacy of the proposed GA is demonstrated by its application to the unimodal, multimodal and nonlinear control problems, respectively. Simulation results show that in the convergence speed to the optimal value, the proposed GA was superior to existing ones, and the performance of GAs with varying probabilities of the crossover and the mutation improved as compared to GAs with fixed probabilities of the crossover and mutation. And it also shows that the NUs function as the building blocks and so the improvement of the proposed algorithm is supported by the building block hypothesis.

• 대한교통학회지
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• 제22권2호
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• pp.43-54
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• 2004

본 연구에서는 인공지능(Artificial Intelligence)방법 중의 하나인 유전자 알고리즘(Genetic Algorithm)을 도로선형최적화 모형개발의 탐색엔진으로 활용하기 위한 핵심도구인 유전자 연산자(Genetic Operator)의 개발과 적용과정을 통해 그 특징과 유용성을 제시하였다. 균일돌연변이 연산자, 직선돌연변이 연산자. 비균일 돌연변이 연산자, 전체 비균일 돌연변이 연산자 등 4개의 돌연변이 연산자가 탐색영역(Search space)의 가능한 모든 부분을 탐험(Exploration)하기 위해 적용되었으며, 단순교차 연산자, 두 개의 점을 이용한 교차 연산자, 산술교차 연산자, 학습교차 연산자 등 4개의 교차 연산자가 노선대안의 우수한 유전형질을 다음세대에 효과적으로 전달(Exploitation)하기 위해 시험되었다. 사례연구와 민감도 분석과정을 통해 유전자 알고리즘 및 개발 적용된 8개 유전자 연산자의 도로선형최적화과정 도입이 우수한 노선대안을 빠르고 효과적으로 탐색함을 알 수 있었으며, 돌연변이 연산자와 교차 연산자의 효과적 조합이 상호보완기능을 통해 탐색능력의 향상에 큰 영향을 끼치는 것으로 파악되었다. 또한, 개발 적용된 연산자 이외에도 새로운 연산자의 개발 가능성이 무한하며, 이는 도로선형최적화에 유전자 알고리즘의 적용이 타당함을 반증함도 주목할 만하다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.257-260
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• 1997

This paper designs the optimal PID controller for load frequency control on 2-area power system. Genetic algorithm is utilized to optimize parameters of PID controller which is applied to power system. Using two-point crossover, uniform crossover and one-point crossover, Search performance of genetic algorithm with each crossover method is considered. In case of load variation in 1-area, the dynamic characteristic of power system is considered. The simulation results show that the proposed PID controller is better control performance than PID controller using Ziegler-Nichols method.

• Computers and Concrete
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• 제11권6호
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• pp.541-569
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• 2013

There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.

• 에너지공학
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• 제2권1호
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• pp.114-122
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• 1993

A model describing the reaction rate and catalyst deactivation in a simplified reaction system was developed to investigate the significance of catalyst pore structure in terms of porosities, porosity ratios, and size ratios of reactants to pores. The model showed that the unimodal catalyst could give a better performance than the bimodal in certain circumstances and the crossover found in the reactivity curves resulted from a trade-off between surface area and diffusivity. Under the assumption of uniform coke buildup, the bimodal catalyst appeared to provide better resistance to deactation than unimodal catalyst.

• 산업경영시스템학회지
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• 제18권33호
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• pp.111-118
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• 1995

This paper suggests a hybrid genetic algorithm for asymmetric traveling salesman problem(TSP). The TSP was proved to be NP-complete, so it is difficult to find optimal solution in reasonable time. Therefore it is important to develope an algorithm satisfying robustness. The algorithm applies dynamic programming to find initial solution. The genetic operator is uniform order crossover and scramble sublist mutation. And experiment of parameterization has been performed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.1006-1010
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• 1995

This paper describes Sexual Reproduction Genetic Algorithm(SRGA) for function optimization. In SRGA, each individual utilize a diploid chromosome structure. Sex cells(gametes) are produced through artificial meiosis in which crossover and mutation occur. The proposed method has two selection operators, one, individual selection which selects the individual to fertilize, and the other, gamete selection which makes zygote for offspring production. We consider the effects of multi-selection and diploidy on search performance. SRGA improves local and global search(exploitation and exploration) and show optimum tracking performance in nonstationary environments. Gray coding is incorporated to transforming the search space and Genic uniform distribution method is proposed to alleviate the problem of premature convergence.

• Advances in Computational Design
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• 제1권3호
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

• Composites Research
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• 제12권4호
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• pp.42-54
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• 1999

섬유-금속 적층 복합재료(FMLC)는 섬유와 금속 박판으로 구성된 새로운 형태의 구조재로 가볍고 우수한 피로 특성을 가지며 금속과 같이 가소성과 충격저항성이 우수하고, 가공성이 뛰어나다. 본 연구에서는 여러 하중 조건하에 있는 섬유-금속 적층 복합재료를 유전자 알고리듬을 이용하여 최적 설계하였다. 전단변형이론에 근거한 유한요소법을 사용하여 적층판을 해석하였으며, 설계변수로 금속판의 강도와 섬유 층의 수에 따른 적층각도를 두었다. 섬유층과 금속판의 적합도 함수로는 각각 Tasi-Hill failure criterion과 Miser yield criterion을 사용하였다. 유전자 알고리듬의 연산자로는 토너먼트 선택과 균일 교배를 사용하였다. 효율적인 진화를 위해 엘리티스트 모델을 사용하며, 높은 정확도를 가진 해를 얻기 위해 크리프 무작위 탐색(creeping random search) 방법을 통해 더 우수한 자손을 얻었다. 여러 가지 하중 조건에 대하여 최적설계 결과를 나타내었으며, 파괴 지수 측면에서 탄소섬유강화복합재료(CFRP)와 비교하였다. 해석 결과 섬유-금속 적층 복합재료는 탄소섬유강화복합재료에 비하여 집중하중이나 분포하중 형태에 대하여 우수한 특성을 보였으며, 파괴 지수의 편차가 적어 예기치 않은 하중에 잘 견딜 것으로 사료된다.

• Journal of Electrical Engineering and Technology
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• 제2권3호
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• pp.312-319
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• 2007

The solution of the power flow is one of the most important problems in electrical power systems. These traditional methods such as Gauss-Seidel method and Newton-Raphson (NR) method have had drawbacks up to now such as initial values, abnormal operating solutions and divergences in heavy loads. In order to overcome theses problems, the power flow solution incorporating genetic algorithm (GA) is introduced in this paper. General operator of genetic algorithm, arithmetic crossover, and non-uniform mutation operator of GA are suggested to solve the power flow problem. While abnormal solution cannot be obtained by a NR method, multiple power flow solution can be obtained by a GA method. With a heavy load, both normal solution and abnormal solution can be obtained by a proposed method. In this paper, a floating number representation instead of the binary number representation is introduced for accuracy. Simulation results have been compared with traditional methods.