• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 춘계학술발표회논문집(1)
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• pp.380-385
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• 1996

The genetic algorithm(GA) is applied to the design of the nuclear power control system. The reactor control system model is described in the LQR configuration. The LQR system order is increased to make the tracking system. The key parameters of the design are weighting matrices, and these are usually determined through numerous simulations in the conventional design. To determine the more objective and optimal weightings, the improved GA is applied. The results show that the weightings determined by the GA yield the better system responses than those obtained by tile conventional design method.

• 한국컴퓨터정보학회논문지
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• 제17권4호
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• pp.129-137
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• 2012

여왕벌의 생식방식을 모방하여 만든 여왕벌 유전자알고리즘은 유전자알고리즘의 성능을 대폭 향상시켰다. 그러나 여왕벌 유전자알고리즘에서는 여왕벌을 하나만사용하여 진화를 수행함으로서 개체들이 지나치게 해당 여왕벌이 있는 쪽으로 몰리는 문제를 발생하였으며 이는 결국 유전자 알고리즘의 성능저하를 가져왔다. 본 논문에서는 이러한 문제를 해결하고자 각 세대에서 가장 적합도가 좋은 여왕벌과 더불어 개체의 적합도가 부모 개체에 비하여 가장 크게 증가한 두 번째 여왕벌을 도입한 다중 여왕벌 진화 알고리즘을 제안한다. 다중 여왕벌을 도입함으로서 개체가 지역 최적해에 빠질 가능성이 줄어들고 지역 최적해에 빠진 경우에도 보다 쉽게 지역 최적해를 빠져나올 수 있게 되어 성능향상이 가능하였다. 4개의 함수최적화 문제에 적용시켜본 결과 본 논문에서 제안한 방법이 기존의 방법보다 대부분의 경우에서 성능이 향상됨을 볼 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2226-2229
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• 2003

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. In GA optimization, neuron spreads were represented in a chromosome. The proposed optimization method was applied to a data set, consisted of 4 different promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The range of neuron spreads was experimentally varied from 0.4 to 1.4 with an increment of 0.1. The GA-GRNN was compared to a conventional GRNN. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. The GA-GRNN significantly improved the total classification sensitivity compared to the conventional GRNN. Also, the GA-GRNN demonstrated an improvement of about 10.1% in the total prediction accuracy. As a result, the proposed GA-GRNN illustrated improved classification sensitivity and prediction accuracy over the conventional GRNN.

• 한국정보통신학회논문지
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• 제9권1호
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• pp.165-171
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• 2005

본 논문에서는 중앙집중형 동적부하균등을 효율적으로 처리하기 위하여 네트워크기반 병렬 유전자 알고리즘을 이용하였다. 기존의 유전자 알고리즘을 적용한 경우와는 달리 클라이언트들에서 최적작업 할당의 탐색을 분산처리하여 중앙 스케줄러의 성능을 향상시킬 수 있었다. 최적해의 수렴속도를 향상시키기 위해 선택연산은 룰렛휠 선택과 엘리트 보존전략을 함께 사용하였고, 염색체 인코딩은 슬라이딩윈도우기법을 이용하였으며 교차연산은 주기교차방법을 이용하였다. 부하균등기법의 유연성 변화에 따른 중앙 스케줄러의 성능을 모의실험한 결과 기존의 방법보다 성능이 향상됨을 확인하였다.

• Structural Engineering and Mechanics
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• 제52권6호
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• pp.1099-1120
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• 2014

In this paper, reliability-based design optimization (RBDO) of structures is addressed. For this purpose, the global search and optimization capabilities of genetic algorithm (GA) are combined with the efficiency and reasonable accuracy of an advanced moment-based finite element reliability method. For performing RBDO, three variants of GA including a real-coded, a binary-coded and an improved binary-coded GA are developed. In these methods, GA performs (finite element) reliability analyses to evaluate reliability constraints. For truss structures which include finite element modeling, reliability constraints are evaluated using finite element reliability analysis. Response sensitivity required for finite element reliability analysis is obtained by direct differentiation method (DDM) rather than finite difference method (FDM). The proposed methods are examined within four standard test examples and real-world design problems. The results illustrate the superiority and efficiency of the improved binary-coded GA. Results also illustrate that DDM significantly reduces the computational cost and improves the efficiency of the optimization procedure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5243-5263
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• 2017

Data center networks provide critical bandwidth for the continuous growth of cloud computing, multimedia storage, data analysis and other businesses. The problem of low link bandwidth utilization in data center network is gradually addressed in more hot fields. However, the current scheduling strategies applied in data center network do not adapt to the real-time dynamic change of the traffic in the network. Thus, they fail to distribute resources due to the lack of intelligent management. In this paper, we present an improved adaptive traffic scheduling strategy utilizing the simulated annealing genetic algorithm (SAGA). Inspired by the idea of software defined network, when a flow arrives, our strategy changes the bandwidth demand dynamically to filter out the flow. Then, SAGA distributes the path for the flow by considering the scheduling of the different pods as well as the same pod. It is implemented through software defined network technology. Simulation results show that the bisection bandwidth of our strategy is higher than state-of-the-art mechanisms.

• Journal of Microbiology and Biotechnology
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• 제12권4호
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• pp.687-691
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• 2002

To enhance the production of flavonoids [baicalin, wogonin-7-Ο-glucuronic acid (GA)], which are secondary metabolites of Scutellaria baicalensis Georgi(G.) plant cells, a multilayer perceptron control system was applied to regulate the substrate feeding in a fed-batch cultivation. The optimal profile for the substrate feeding rate in a fed-batch culture of S. baicalensis G. was determined by simulating a kinetic model using a genetic algorithm. Process variable profiles were then prepared for the construction of a multilayer perceptron controller that included massive parallelism, trainability, and fault tolerance. An error back-propagation algorithm was applied to train the multiplayer perceptron. The experimental results showed that neurocontrol incorporated with a genetic algorithm improved the flavonoid production compared with a simple fuzzy logic control system. Furthermore, the specific production yield and flavonoid productivity also increased.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1089-1102
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• 2017

In software systems, it has been observed that a fault is often caused by an interaction between a small number of input parameters. Even for moderately sized software systems, exhaustive testing is practically impossible to achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a technique to select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to the fault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets using genetic algorithms. The performance of the algorithm is improved by creating an initial solution using the overlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve their efficiency. Furthermore, the mutation operator is improved by using a combination of three mutation strategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was also conducted. It has been shown experimentally that the proposed approach generates faster results by achieving higher percentage coverage in a fewer number of generations. Additionally, the size of the mixed covering arrays was reduced in one of the six benchmark problems examined.

• 한국산업정보학회논문지
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• 제10권4호
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• pp.16-25
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• 2005

본 논문에서는 최적의 이진 위상 컴퓨터형성홀로그램을 설계하기 위해 SA 및 GA를 합성한 새로운 방법을 제안하였다. 블럭단위 탐색을 하는 GA의 교배연산 및 돌연변이 연산과정 후에 화소단위의 면밀한 탐색을 하는 SA 알고리듬을 삽입하므로써 BPCGH의 성능을 개선시켰다. 컴퓨터 시뮬레이션에서 제안된 합성 반복 알고리듬이 기존의 SA 알고리듬보다 회절효율이 향상됨을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권2호
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• pp.121-126
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• 2006

In the recent years, the development of computer-controlled suspension dampers and actuators has improved the trade-off between the vehicle handling and ride comfort, and has led to the development of various damper control policies. The skyhook control is an effective control strategy for suppressing vehicle vibration. In this study, a fuzzy skyhook control is proposed and tuned by a genetic algorithm to improve ride comfort. The proposed fuzzy skyhook control is applied to a quarter-car model in order to compare its performance with continuous skyhook suspensions. To obtain optimized fuzzy skyhook control, scale factors and in-out membership functions are tuned by a genetic algorithm. The simulation results show that the fuzzy skyhook control offers more effective suspension performance over the continuous skyhook control.