• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.48-57
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• 2009

This paper presents optimal design of direct-driven PM wind generator using MADS (Mesh Adaptive Direct Search). Optimal design of the direct-driven PM Wind Generator, combined with MADS and FEM (Finite Element Method), has been performed to maximize the Annual Energy Production (AEP) over the whole wind speed characterized by the statistical model of the wind speed distribution. In particular, the newly applied MADS contributes to reducing the computation time when compared with Genetic Algorithm (GA) implemented with the parallel computing method.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.137-141
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• 2000

This paper presents an application of a new Adaptive Genetic Algorithms(AGA) to solve the Optimal Routing problem(ORP) for distribution system planning. In general, since the ORP is modeled as a mixed integer problem with some various mathematical constraints, it is hard to solve the problem. In this paper, we proposed a new adaptive strategy in GA to overcome the premature convergence and improve the convergence efficiency. And for these purposes, we proposed a fitness function suited for the ORP. In the proposed AGA, we used specially designed adaptive probabilities for genetic operators to consider the characteristics of distribution systems that are operated under radial configuration. The proposed algorithm has been tested in sample networks and the results are presented.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1177-1179
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• 1999

This paper presents a design of artificial neural network power system stabilizer(ANNPSS) using adaptive evolutionary algorithm(AEA). We have proposed an adaptive evolutionary algorithm which uses both a genetic algorithm(GA) and an evolution strategy(ES), useing the merits of two different evolutionary computations. ANNPSS shows better control performances than conventional power system stabilizer(CPSS) in three-phase fault with heavy load which is used when tuning ANNPSS. To show the robustness of the proposed ANNPSS, it is applied to damp the low frequency oscillation caused by disturbances such as three-phase fault with normal and light load. the proposed ANNPSS shows better robustness than CPSS.

• Journal of Industrial Technology
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• v.19
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• pp.209-216
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• 1999

In this paper, we proposed a modofied genetic algorithm to minimize misclassification rate for determining the codebook. Genetic algorithms are adaptive methods which may be used solve search and optimization problems based on the genetic processes of biological organisms. But they generally require a large amount of computation efforts. GAVQ can choose the optimal individuals by genetic operators. The position of individuals are optimized to improve the recognition rate. The technical properties of this study is that prevents us from the local minimum problem, which is not avoidable by conventional VQ algorithms. We compared the simulation result with Matlab using phoneme data. The simulation results show that the recognition rate from GAVQ is improved by comparing the conventional VQ algorithms.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.2
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• pp.9-15
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• 2001

This paper describes a new method of Genetic Algorithms for Adaptive Fuzzy Control System. Previous works using a Multi-population Genetic Algorithm have divided chromosome into two components, rule sets and membership functions. However, in this case bad rule sets disturb optimization in good rule sets and membership functions. A new method for a Multi population Genetic Algorithm suggests three components, good rule sets, bad rule sets, and membership functions. To show the effectiveness of this method, fuzzy controller is applied to a Truck Backing Problem. Results of the computer simulation show good adaptation of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.207-210
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• 2012

Genetic algorithms(GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of GA, this paper proposes an adaptive partitioning-based genetic algorithm. The partitioning method, which enables GA to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and the optimization of fuzzy controller for the control of an inverted pendulum.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.203-208
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• 2002

In case of the problems with multiple plants, alternative operation sequence, alternative machine, setup time, and transportation time between plants, we need a robust methodology for the integration of process planning and scheduling in supply chain. The objective of this model is to minimize the tardiness and to maximize the resource utilization. So, we propose a multi-objective model with limited-capacity constraint. To solve this model, we develope an efficient and flexible model using adaptive genetic algorithm(AGA), compared to traditional genetic algorithm(TGA)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• Journal of the Korean Institute of Navigation
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• v.24 no.5
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• pp.361-371
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• 2000

This paper treats a genetic algorithm for ship scheduling problem in set packing formulation. We newly devised a partition based representation of solution and compose initial population using a domain knowledge of problem which results in saving calculation cost. We established replacement strategy which makes each individual not to degenerate during evolutionary process and applied adaptive mutate operator to improve feasibility of individual. If offspring is feasible then an improve operator is applied to increase objective value without loss of feasibility. A computational experiment was carried out with real data and showed a useful result for a large size real world problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.391-394
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• 2002

진화 연산(Genetic Algorithm)은 최적화 분야에서 사용되는 강력하면서도 일반적인 방법이다. 이러한 진화 연산의 일반성은 진화 연산에서 사용되는 기본 연산자들이 문제에 대한 정보를 필요로 하지 않는 것에 기인하고 있기에, 실제 구현시에는 여러 파라미터들을 문제에 맞게 정해 줌으로써 성능 향상을 죄할 수 있다. 이러한 파라미터의 조절은 보통 시행착오를 거쳐 행해지나, 실행시에 동적으로 파라미터를 학습하는 적응적 진화 연산도 연구되어 왔다. 본 논문에서는 진화 연산에서의 파라미터 학습 과정을 강화 학습 과정으로 공식화하고 강화 학습을 사용한 적응적 진화 연산 구현을 제안한다.