• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집B
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• pp.650-654
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• 2000

Particle Imaging Velocimetry (PIV) is becoming one of essential methods to measure velocity fields of fluid flows. In this paper, a genetic algorithm capable of tracking same particle pairs on two separated images is introduced. The fundamental of the developed technique is based on that on-to-one correspondence is found between two tracer particles selected in two image planes by taking advantage of combinatorial optimization of the genetic algorithm. The fitness function controlling reproductive success in the genetic algorithm is expressed by physical distances between the selected tracer particles. The capability of the developed genetic algorithm is verified by a computer simulation on a farced vortex flow.

• 한국시뮬레이션학회논문지
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• 제26권4호
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• pp.1-9
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• 2017

유전 알고리즘은 생물 유전학에 기본 이론을 두는 전역 탐색 알고리즘으로, 산업, 뉴럴 네트워크, 웹, 그리고 국방 등의 분야에서 활발히 사용되고 있다. 하지만 기존의 유전 알고리즘은 염색체의 개수가 고정되어 있는 형태여서 시뮬레이션 도중 초기에 주어진 상황보다 더 복잡한 상황이 주어질 수 있는 경우에는 적용이 힘들다는 한계점이 존재한다. 본 연구에서는 이를 극복하기 위해서 염색체 비분리를 적용한 가변 염색체 유전 알고리즘을 제안하였다. 그리고 염색체 수의 변화가 시뮬레이션 결과에 영향을 미치는 것을 확인하기 위하여 대 잠수함 HVU 호위 임무 시뮬레이션에 염색체 비분리를 적용한 가변 염색체 유전 알고리즘을 적용하였다. 시뮬레이션 결과 기존의 유전 알고리즘과는 달리 가변 염색체 유전 알고리즘에서는 더 복잡한 전술이 더 일찍 등장하였으며, 그에 따라 염색체 수가 증가하는 방향으로 진화가 일어나는 것을 확인할 수 있었다.

• Genomics & Informatics
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• 제14권4호
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2003년도 춘계학술대회 논문집
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• pp.22-26
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• 2003

This paper presents a genetic algorithm based autonomic movement algorithm for an autonomous underwater vehicle(AUV) and verified it to simulation. Also, developed program that can do simulation on two dimension and three dimension in seabed environment. The presented algorithm is applicable to a escape from the recursive search and a development of obstacle avoidance system.

• 대한산업공학회지
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• 제39권6호
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• pp.461-472
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• 2013

In this paper, we deal with a simulation-based reliability and maintainability design problem of a warship and want to determine the optimal values of MTBF and MTTR of all units and ALDT of the warship. The system availability and life cycle cost are used as optimization criteria and estimated by simulation. A hybrid genetic algorithm with a heuristic method is proposed to find near-optimal solutions and numerical examples are also studied to investigate the effect of model parameters to the optimal solutions and compare with a general genetic algorithm.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 1998년도 추계학술대회 및 정기총회
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• pp.142-146
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• 1998

In this paper, we suggests a WIP(work in process) of FMS analysis methods based on the Genetic algorithm. We conjoined both the assignment and the scheduling problem in order to create a new representation scheme for a chromosome and a mutation operators.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• 터널과지하공간
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• 제15권2호
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• pp.129-136
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• 2005

터널이나 지하구조물의 건설시 필요한 지보의 설계는 보통 시추에 의한 지반조사결과에 기초하고 있다. 지반조사 자료가 부족한 지역에서의 암반 물성을 보다 객관적이고 추계학적(stochastic)으로 예측하기 위해 유전알고리즘(genetic algorithm)과 조건부 모사 기법(conditional simulation)을 사용하였다. 지구통계학적 모델링의 방법으로 조건부 모사를 실시한 후에 공간상관관계의 최적화과정을 통해 암반 물성을 구하였다. 유전알고리즘을 이용할 경우 크리깅에 의한 분산의 감소 현상을 극복하고 확률적으로 값을 제시할 수 있었다. 또한 30번의 확률적 등가치(equi-probable) 모사를 통해 유전알고리즘으로 구한 값의 불확실성을 정량적인 확률분포 값으로 제시하였고, 교차검증(cross validation) 방법으로 유전알고리즘의 신뢰도를 검증하였다.

• Genomics & Informatics
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• 제20권1호
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• 한국정보통신학회논문지
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• 제5권5호
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• pp.977-984
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• 2001

본 논문에서는 유전자-퍼지 제어 알고리즘에 대하여 논의하고 그 성능을 평가하였다. 이 알고리즘은 퍼지 논리와 유전자알고리즘의 융합된 형태이며, 제어 대상으로는 도립진자 시스템을 모델링 하였다. 퍼지 제어기는 두 개의 입력과 한 개의 출력 변수를 설계하기 위해 적용되며, GA(Genetic Algorithm)는 퍼지 규칙과 소속 함수를 선택, 교차, 돌연변이의 진화 연산을 통해 최적화한다. 컴퓨터 시뮬레이션에 퍼지 제어의 경우 초기 함수 f(0.3, 0.3)일 때 최대 언더슈트가 $-5.0 \times 10^{-2}[rad]$, 최대 오버슈트가 $3.92\times10^{-2}[rad]$으로 측정되었으나, 유전자 퍼지 알고리즘의 경우 최대 오버슈트와 언더슈트가 각각 0.0[rad]으로 측정되었다. 또한 정상상태 도달시간이 퍼지제어의 경우 2.12[sec], 유전자-퍼지 알고리즘은 1.32[sec]로 비교적 안정적으로 나타났다. 컴퓨터 시뮬레이션으로 이 알고리즘을 도립진자 시스템에 적용시키고, 그 성능의 우수성과 효율성을 증명하였다.