• 대한전기학회논문지:시스템및제어부문D
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• 제52권7호
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제52권7호
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• 생명과학회지
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• 제18권9호
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• pp.1284-1289
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• 2008

본 연구에서는 EVOP-factorial system을 활용하여 매실식초의 최적 발효 조건을 찾고자 하였다. 3-7% 사이의 에탄올 농도(r=-0.5166)와 glucose 농도(r=-0.5061)는 10% 유의수준에서 산도에 영향을 미치는 것으로 확인되었다. 24-$33^{\circ}C$의 범위 내에서 발효온도는 매실식초의 산도 증가에 유의적인 영향을 미치지 못하는 것으로 확인되었다(r=0.1082). EVOP- factorial system을 활용하여 얻은 매실식초의 최적 발효조건은 발효 온도: $30^{\circ}C$, 에탄올 농도: 4%, 포도당 농도: 0.2%로 결정되었으며, 최적 조건에서의 산도값은 6.365%로 set 1의 중심점에서 나타난 산도값 5.4%에 비해 1.0%정도 높아졌다. 본 연구결과는 EVOP-factorial design을 이용하여 매실식초의 최적 발효 조건을 확인한 최초의 시도이다.

• 대한전기학회논문지:전력기술부문A
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• 제48권12호
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• pp.1498-1506
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• 1999

In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

• 대한산업공학회지
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• 제30권2호
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• pp.130-145
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• 2004

This paper addresses the integrated problem of process planning and scheduling in FMS (Flexible Manufacturing System). The integration of process planning and scheduling is important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called asymmetric multileveled symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. Efficient genetic representations and operator schemes are considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and existing evolutionary algorithms. The experimental results show that the proposed algorithm outperforms the compared algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.50.2-50
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• 2001

Optimal control problems are notoriously difficult to solve either analytically or numerically except for limited cases of having simple dynamics. Evolutionary programming is a promising method of solving various optimal control problem arising in practice since it does not require the expression of Lagrange´s adjoint system and that it can easily implement the inequality constraints on the control variable, In this paper, evolutionary programming is combined with spline method, so the smoother control profile and the Jumping times could be obtained. The optimal profiles obtained by the proposed method are compared with exact solution if it is available. With more complicated model equation, the proposed method showed better performance than other researchers´. It is demonstrated that the evolutionary programming with spline method can ...

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.410-415
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• 2004

The oscillation of the fluid caused by external forces is call ed sloshing, which occurs in moving vehicles with contained liquid masses, such as trucks, railroad cars, aircraft, and liquid rocket. This sloshing effect could be a severe problem in vehicle stability and control. In this study, the optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively the artificial neural network(ANN) and genetic algorithm. An artificial neural network is used for the analysis of sloshing and genetic algorithm is adopted as optimization algorithm. As a result of optimization design, the optimized size and location of the baffle is presented

• 한국컴퓨터정보학회논문지
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• 제23권9호
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• 한국미생물·생명공학회지
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• 제48권4호
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• pp.423-428
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• 2020

In vitro evolution is a powerful technique for the engineering of yeast strains to study cellular mechanisms associated with evolutionary adaptation; strains with desirable traits for industrial processes can also be generated. There are two distinct approaches to generate evolved strains in vitro: the sequential transfer of cells in the stationary phase into fresh medium or the continuous growth of cells in a chemostat bioreactor via the constant supply of fresh medium. In culture, evolutionary forces drive diverse adaptive mechanisms within the cell to overcome environmental or intracellular stressors. Especially, this engineering strategy has expanded to the field of human cell lines; the understanding of such adaptive mechanisms provides promising targets for the treatment of human genetic diseases and cancer. Therefore, this technology has the potential to generate numerous industrial, medical, and academic applications.

• 전자공학회논문지C
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• 제35C권9호
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• pp.81-92
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• 1998

실수형 최적화 문제의 전역 최적해를 빠르고 정확하게 찾을 가능성을 높이기 위해, 서로 다른 진화특성을 가지는 여러 부집단들을 사용한 새로운 하이브리드 기법이 제안된다. 제안된 알고리듬은 세 개의 부집단을 사용하는데, 복잡한 적합도 함수를 가지는 문제에서 좋은 성능을 보이는 NPOSA 알고리듬이 두개의 부집단에 적용되고, 진화 방향과 크기가 조절되는 자기 적응 진화 알고리듬이 나머지 하나의 부집단에 적용되었다. 각 부집단들은 서로 다른 방법으로 진화하며 부집단들간의 상호교류를 통해 전역 최적해로 빠르게 도달하게 한다. 이 기법의 효율성은 몇 개의 표준 테스트 문제들을 사용하여 검증하였다. 마지막으로, 제안한 알고리듬이 실제 문제에 적용 가능함을 보이기 위해 카메라 파라메터의 최적값을 찾는 문제에 적용하였다. 보정 블럭에서 측정된 특징점들을 사용하여 오차 함수를 정의한 후, 하이브리드 방법이 그 오차 함수를 최소화하는 카메라 파라메터의 값을 찾을 수 있음을 보였다.