• Structural Engineering and Mechanics
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• v.70 no.5
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.202-204
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• 2005

This paper is proposed genetic algorithm tuning(GAT) controller for high performance of induction motor drive. We employed GA to the classical PI controller. The approach having ability for global optimization and with good robustness, is expected to overcome some weakness of conventional approaches and to be more acceptable for industrial practices. The control performance of the GAT PI controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.325-326
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• 2009

본 논문에서는 효율적인 인공지능 알고리즘인 유전자 알고리즘(GA)을 이용한 문서 클러스터링 시스템을 제안한다. 일반적으로 클러스터링 알고리즘에 가장 많이 사용되는 K-Means는 임의로 결정되는 초기 센트로이드 벡터에 따라 그 성능이 많이 달라지는 것을 볼 수 있다. 이에 본 논문에서는 유전자 알고리즘을 이용하여 안정적이면서도 높은 성능을 보여주는 클러스터링 알고리즘을 개발하였다. 제안한 클러스터링 알고리즘의 성능 평가를 위하여 HANTEC 2.0과 문서 범주화 집단 데이터 셋을 사용하였다. 제안된 방법은 효율적이고 빠른 K-Means를 이용한 클러스터링 알고리즘에 비하여 훨씬 뛰어난 성능을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.78.5-78
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• 2002

We propose an evolutionary reinforcement learning (RL) system with time-varying parameters that can deal with a dynamic environment. The proposed system has three characteristics: 1) It can deal easily with a dynamic environment by using time-varying parameters; 2) The division of state space is acquired evolutionarily by genetic algorithm (GA); 3) One does not have to design the rules constructing an agent in advance. So far many RL systems have been proposed. These systems adjust constant or non time-varying parameters; by those systems it is difficult to realize appropriate behavior in complex and dynamic environment. Hence, we propose the RL system whose parameters can vary temporally. T...

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.261-267
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• 2023

A common approach to the problem of predicting student test scores is based on the student's previous educational history. In this study, high school transcripts of about two thousand candidates, who took the High-school Student Assessment (HSA) were collected. The data were estimated through building a regression model - Random Forest and optimizing the model's parameters based on Genetic Algorithm (GA) to predict the HSA scores. The RMSE (Root Mean Square Error) measure of the predictive models was used to evaluate the model's performance.

• Earthquakes and Structures
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• v.25 no.1
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• pp.69-78
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• 2023

Life-cycle performance analysis of a reinforced concrete box section bridge was generated. Moreover, Monte Carlo simulation with important sampling (IS) was used to simulate the bridge material and load uncertainties. The bridge deterioration model was generated with the basic probabilistic principles and updated according to the measurement data. A genetic algorithm (GA) with the response surface model (RSM) was used to determine the deterioration rate. The importance of health monitoring systems to sustain the bridge to give services economically and reliably and the advantages of fiber-optic sensors for SHM applications were discussed in detail. This study showed that the most effective loss of strength in reinforced concrete box section bridges is corrosion of the reinforcements. Due to reinforcement corrosion, the use of the bridge, which was examined, could not meet the desired strength performance in 25 years, and the need for reinforcement. In addition, it has been determined that long-term health monitoring systems are an essential approach for bridges to provide safe and economical service. Moreover the use of fiber optic sensors has many advantages because of the ability of the sensors to be resistant to environmental conditions and to make sensitive measurements.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.166-168
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• 2015

Line of Balance (LOB) method is suitable to schedule construction projects composed of repetitive activities. Since existing LOB based repetitive project scheduling methods are deterministic, they do not lend themselves to handle uncertainties involved in repetitive construction process. Indeed, existing LOB scheduling dose not handle variability of project performance indicators. In order to bridge the gap between reality and estimation, this study provides a stochastic LOB based scheduling method that allows schedulers for effectively dealing with the uncertainties of a construction project performance. The proposed method retrieves an appropriate probability distribution function (PDF) concerning project completion times, and determines favorable start times of activities. A case study is demonstrated to verify and validate the capability of the proposed method in a repetitive construction project planning.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.16-24
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• 1997

This paper proposes a modified GA whose individuals have their own ages. Thus, a chromosome will die only when the age becomes zero, as a result, the population size of this method increases according to the generations. This helps a GA to preserve the good characteristics of a few chromosomes during several generations if the ages are evaluated with fitness values. As a result, the performance of the method is better than that of existing ones. A multi-modal function optimization problem is employed to simulate the performance of this method. To show the effective:~esso f ageing paradigm, three ageing evaluation methods are introduced. A paper whose itlea is similar to that of ours have been published in a conference. We also experimented a method that showed the best performance in the paper. Original simple GA was also experimented and the performance is compared with others. However, the perforniance of the previous method shows worse than that of our methods in some aspects because the previous method didn't take the fitness value into account in the selection process.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.525-536
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• 2004

Gene expression profile is numerical data of gene expression level from organism, measured on the microarray. Generally, each specific tissue indicates different expression levels in related genes, so that we can classify disease with gene expression profile. Because all genes are not related to disease, it is needed to select related genes that is called feature selection, and it is needed to classify selected genes properly. This paper Proposes GA based method for searching optimal ensemble of feature-classifier pairs that are composed with seven feature selection methods based on correlation, similarity, and information theory, and six representative classifiers. In experimental results with leave-one-out cross validation on two gene expression Profiles related to cancers, we can find ensembles that produce much superior to all individual feature-classifier fairs for Lymphoma dataset and Colon dataset.