• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.567-575
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• 2001

A methodology is developed for designing the minimum-cost water distribution network. The method is based on network simulations and an optimization scheme using genetic algorithms. Being a stochastic optimization scheme, genetic algorithms have advantages over the conventional search algorithms in solving network problems known for their nonlinearities and herculean computational costs. While existing methods focus on the design of either entirely new or parallel augmentation of network systems, the proposed method can be applied to problems having both new branches of tree-type and paralle augmentation in loops. The applicability of the method was shown through a case study for Baekryeon water supply system. The optimized design resulted in the maximum 5.37% savings compared to the conventional design without optimization, while meeting the hydraulic constraints.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.601-614
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• 2006

In this paper, a study on task assignment strategies for a complex real-time network system is presented. Firstly, two task assignment strategies are proposed to improve previous strategies. The proposed strategies assign tasks with meeting end-to-end real-time constraints, and also with optimizing system utilization through period modulation of the tasks. Consequently, the strategies aim at the optimizationto optimize of system performance with while still meeting real-time constraints. The proposed task assignment strategies are devised using the genetic algorithmswith heuristic real-time constraints in the generation of new populations. The strategies are differentiated by the optimization method of the two objectives-meeting end-to-end real-time constraints and optimizing system utilization: the first one has sequential genetic algorithm routines for the objectives, and the second one has one multiple objective genetic algorithm routine to find a Pareto solution. Secondly, the performances of the proposed strategies and a well-known existing task assignment strategy using the BnB(Branch and Bound) optimization are compared with one other through some simulation tests. Through the comparison of the simulation results, the most adequate task assignment strategies are proposed for some as system requirements-: the optimization of system utilization, the maximization of running tasktasks, and the minimization of the number of network node nodesnumber for a network system.

• Proceedings of the KIEE Conference
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• 2006.10a
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• pp.145-146
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• 2006

본 논문은 부분방전(PD: Partial Discharge)의 패턴인식 확률 극대화를 목적으로 신경망(NN: Neural Network) 파라미터 중에서 은닉층 뉴런의 수, 모멘텀(momentum)의 Step size와 Decay rate 를 최적화하기 위하여 유전 알고리즘(GA: Genetic Algonthm)을 적응하였다. 실험적 연구의 대상으로서, GIS(Gas Insulated Switchgear)사고의 주요 원인으로 보고되어있는 결함들을 인위적으로 모의한 16개 Test cell을 이용하여 부분방전을 발생시켰다. 부분방전 신호는 본 연구팀이 개발한 센서를 이용하여 검출되어 데이터베이스가 구축되어 그로부터 추출된 학습 데이터들의 학습에 다음과 같은 5가지 신경망 모델이 적응되었다: Multilayer Perception (MLP), Jordan-Elman Network (JEN), Recurrent Network (RN), Self-Organizing Feature Map (SOFM), Time-Lag Recurrent Network (TLRN). 유전 알고리즘 적용 효율성을 분석하기 위하여 동일한 데이터를 이용하여 다음과 같은 두 가지 방법을 적용한 결과를 상호 비교하였다. 우선 상기 선택된 모델만 적용하였고 다근 하나는 상기 모델과 Genetic Algorithm이 동시에 적용되었다. 모든 모델에 대하여 학습오차와 패턴 분류 확률을 비교한 결과, 유전 알고리즘 적응 시 부분방전 패턴인식 확률이 향상되었음이 확인되어 향후 신뢰성 있는 GIS 부분방전 진단기술에 활용될 수 있을 것으로 사료된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.105-111
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• 1999

This Paper is about how to deside the best parameter of PID controller, using Genetic Algorithms and Neural Networks. Control by Genetic Algorithms, which is off-line pass, has weakness for disturbance. So we want to improve like followings by adding Neural Network to controller and putting it on line. First we find PID parameter by Genetic Algorithms in forward pass of Neural Network and set the best output condition according to the increasing number of generation. Second, we explain the adaptability for disturbance with simulation by correcting parameter by backpropagation learning rule by using the learning ability of Neural Network.

• Bulletin of the Korean Chemical Society
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• v.29 no.4
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• pp.833-841
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• 2008

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC’s) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC’s, the genetic algorithm was employed for selection of the best set of extracted PC’s for PC-MLR and PC-ANN models. The models were generated using fifteen PC’s as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and $12.77{^{\circ}C}$, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = $40.7{^{\circ}C}$). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.729-731
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• 2016

The efficiency of genetic algorithm may be improved, as genetic generation increases. However, a number of algorithms are needed until desired results are obtained. In addition, if Hoc unit increases when linked to a network, there may be a need to compare with all the cases, which would result in many algorithms at a time, and if a single process deals with such algorithms, the efficiency will decrease. Therefore, efficiency of overall network will be decreased in the end. Proposal algorithm in this thesis introduced cells, units of router group, for distributed processing of previous genetic algorithm. This thesis presented ways to reduce search delay time of overall network through cell-based genetic algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.737-741
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• 1994

We show an application of a genetic algorithm to, control systems including neural networks. Genetic algorithms are getting more popular nowadays because of their simplicity and robustness. Genetic algorithms are global search techniques for optimization and many other problems. A feed-forward neural network which is widely used in control applications usually learns by error back propagation algorithm(EBP). But, when there exist certain constraints, EBP can not be applied. We apply a modified genetic algorithm to such a case. We show simulation examples of two cart-pole nonlinear systems: single pole and double pole.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.767-772
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• 2018

Recently, some meta-heuristic algorithms, such as GA(Genetic Algorithm) and GP(Genetic Programming), have been used to optimize CNN(Convolutional Neural Network). The CNN, which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, the recent attempts to automatically construct CNN architectures are investigated and analyzed. First, two GA based methods are summarized. One is the optimization of CNN structures with the number and size of filters, connection between consecutive layers, and activation functions of each layer. The other is an new encoding method to represent complex convolutional layers in a fixed-length binary string, Second, CGP(Cartesian Genetic Programming) based method is surveyed for CNN structure optimization with highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The comparison for three approaches is analysed and the outlook for the potential next steps is suggested.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.99-107
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• 2022

Urban Air Mobility (UAM) such as a drone taxi is one of the future transportations that have recently been attracting attention. Along with the construction of an urban terminal, an accurate landing system for UAM is also essential. However, in urban environments, reliable Global Navigation Satellite Systems (GNSS) signals cannot be received due to obstacles such as high-rise buildings which causes multipath and non-line of sight signal. Thus, the positioning result in urban environments from the GNSS signal is unreliable. Consequently, we propose the Ultra-Wideband (UWB) network to assist the soft landing of UAM on a vertiport. Since the positioning performance of UWB network depends on the layout of UWB anchors, it is necessary to optimize the layout of UWB anchors. In this paper, we propose a two-steps genetic algorithm that consists of binary genetic algorithm involved multi objectives fitness function and integer genetic algorithm involved robust solution searching fitness function in order to optimize taking into account Fresnel hole effects.