• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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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.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.3
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• pp.128-132
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• 2003

This paper presents the optimum design of a Linear Induction Motor (LIM) using Genetic algorithm, Niching Genetic algorithm and Neural Network. The design variables are optimized by different optimization methods and the results are discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.3
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• pp.135-142
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• 2011

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

• Journal of Mechanical Science and Technology
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• v.16 no.8
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• pp.1033-1038
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• 2002

Genetic alsorithm (GA) , compared to the gradient-based optimization, has advantages of convergence to a global optimized solution. The genetic algorithm requires so many number of analyses that may cause high computational cost for genetic search. This paper proposes a personal computer network programming based on TCP/IP protocol and client-server model using socket, to improve processing speed of the genetic algorithm for optimization of composite laminated structures. By distributed processing for the generated population, improvement in processing speed has been obtained. Consequently, usage of network-based genetic algorithm with the faster network communication speed will be a very valuable tool for the discrete optimization of large scale and complex structures requiring high computational cost.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.71.2-71
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• 2001

This paper deals with nonlinear system modelling using neural network and genetic algorithm. Application of neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. In this paper, We optimize neural network structure using genetic algorithm. The genetic algorithm uses binary coding for neural network structure and search for optimal neural network structure of minimum error and response time. Through extensive simulation, Optimal neural network structure is shown to be effective for ...

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.2
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• pp.76-82
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• 2001

This paper presents a approach for a service restoration and optimal reconfiguration of distribution network using Genetic algorithm(GA) and Tabu search(TS) method. Restoration and reconfiguration problems in distribution network are difficult to solve in short times, because distribution network supplies power for customers combined with many tie-line switches and sectionalizing switches. Furthermore, the solutions of these problems have to satisfy radial operation conditions and reliability indices. To overcome these time consuming and sub-optimal problem characteristics, this paper applied Genetic-Tabu algorithm. The Genetic-Tabu algorithm is a Tabu search combined with Genetic algorithm to complement the weak points of each algorithm. The case studies with 7 bus distribution network showed that not the loss reduction but also the reliability cost should be considered to achieve the economic service restoration and reconfiguration in the distribution network. The results of suggested Genetic-Tabu algorithm and simple Genetic algorithm are compared in the case study also.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.10-13
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• 2001

Nonlinear Function Approximation of Moduled Neural Network Using Genetic Algorithm Neural Network consists of neuron and synapse. Synapse memorize last pattern and study new pattern. When Neural Network learn new pattern, it tend to forget previously learned pattern. This phenomenon is called to catastrophic inference or catastrophic forgetting. To overcome this phenomenon, Neural Network must be modularized. In this paper, we propose Moduled Neural Network. Modular Neural Network consists of two Neural Network. Each Network individually study different pattern and their outputs is finally summed by net function. Sometimes Neural Network don't find global minimum, but find local minimum. To find global minimum we use Genetic Algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.625-628
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• 1998

This paper presents an application of genetic algorithms(GAs) for optimal configuration of distribution network. Three problems have been used to show how genetic algorithms are modified and applied. Solutions to the problems are found by minimizing the cost function which is directly related with balancing the loads. Simulation results show that genetic algorithms are technically feasible if they are tailored to meet the needs of real problems.