• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.652-658
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• 1998

We introduce the concept of a bi-population scheme for real-coded GAs consisting of an explorer sub-Ga and an exploiter sub-GA. The explorer sub-GA mainly performs global exploration of the search space, and incorporates a restart mechanism to help avoid being trapped at local optima. The exploiter sub-GA performs exploitation of fit local areas of the search space around the neighborhood of the best-so-far solution. Thus the search function of the algorithm is divided. the proposed technique exhibits performance significantly superior to standard GAs on two complex highly multimodal problems.

• 산업기술연구
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• 제31권B호
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2002년도 추계학술발표대회 논문집
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• pp.233-236
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• 2002

An efficient method was developed in this study to obtain optimal stacking sequences, thicknesses, and minimum weights of stiffened laminated composite shells under combined loading conditions and stiffener layouts using genetic algorithms (GAs) and finite element analyses. Among many parameters in designing composite laminates determining a optimal stacking sequence that may be formulated as an integer programming problem is a primary concern. Of many optimization algorithms, GAs are powerful methodology for the problem with discrete variables. In this paper the optimal stacking sequence was determined, which gives the maximum critical buckling load factor and the minimum weight as well. To solve this problem, both the finite element analysis by ABAQUS and the GA-based optimization procedure have been implemented together with an interface code. Throughout many parametric studies using this analysis tool, the influences of stiffener sizes and three different types of stiffener layouts on the stacking sequence changes were throughly investigated subjected to various combined loading conditions.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.33-36
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• 2004

The process modeling for the growth rate in pulsed laser deposition(PLD)-grown ZnO thin films is investigated using neural networks(NNets) and the process recipes is optimized via genetic algorithms(GAs). D-optimal design is carried out and the growth rate is characterized by NNets based on the back-propagation(BP) algorithm. GAs is then used to search the desired recipes for the desired growth rate. The statistical analysis is used to verify the fitness of the nonlinear process model. This process modeling and optimization algorithms can explain the characteristics of the desired responses varying with process conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.152-154
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• 2005

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed genetic algorithms-based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• 정보처리학회논문지B
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• 제17B권2호
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• pp.149-156
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• 2010

유전자알고리즘의 조숙수렴현상(premature convergence phenomenon)은 유전자알고리즘의 성능을 크게 저하시키기 때문에 이 문제를 해결하는 것이 성능향상에 크게 영향을 준다. 본 논문에서는 유전자알고리즘의 조숙수렴현상을 완화하여 성능을 향상시키기 위한 선택적 돌연변이 방법을 제안한다. 선택적 돌연변이에서는 유전자알고리즘 개체의 등급에 따라서 염색체의 특정영역에 비트를 추가적으로 돌연변이 시킨다. 이렇게 함으로서 등급이 낮은 개체는 표현형 상에서 많은 변화가 일어나고 등급이 높은 개체는 작은 변화가 일어나게 된다. 결국 좋은 개체는 그 주변을 세부적으로 탐색하며 좋지 못한 개체는 새로운 영역을 탐색할 기회가 높아지게 되어 조숙수렴현상을 완화하면서 성능향상을 꾀할 수 있게 된다. 성능향상을 측정하기 위하여 4개의 대표적 함수 최적화 문제에 적용해서 제안한 방법의 성능을 측정하였다. 실험결과 기존의 유전자알고리즘보다 성능이 크게 향상됨을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.146-148
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• 2005

In this paper, we propose a genetically optimized identification of information granulation(IG)-based fuzzy model. To optimally design the IG-based fuzzy model we exploit a hybrid identification through genetic alrogithms(GAs) and Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the seleced input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the inital parameters are tuned effectively with the aid of the genetic algorithms and the least square method. And also, we exploite consecutive identification of fuzzy model in case of identification of structure and parameters. Numerical example is included to evaluate the performance of the proposed model.

• Journal of Advanced Marine Engineering and Technology
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• 제31권1호
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• pp.58-66
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• 2007

Genetic Algorithms (GA) have some difficulties in practical applications because of too many function evaluations. To overcome these limitations, an approximated modeling method such as Response Surface Modeling(RSM) is coupled to GAs. Original RSM method predicts linear or convex problems well but it is not good for highly nonlinear problems cause of the average effect of the least square method(LSM). So the locally approximated methods. so called as moving least squares method(MLSM) have been used to reduce the error of LSM. In this study, the efficient evolutionary GAs tightly coupled with RSM with MLSM are constructed and then a 2-dimensional inviscid airfoil shape optimization is performed to show its efficiency.