• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.305-308
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• 2005

본 논문은 유전자 알고리즘 기반 퍼지 다항식 뉴럴네트워크(Genetic Algorithm-based Fuzzy Polynomial Neural Networks ; GAs-based FPNN)를 이용하여 비선형 데이터의 최적화 추론 알고리즘을 제안한다. FPNN의 각 노드는 GMDH와 퍼지규칙을 기초로 만들었다. FPNN의 각 노드는 퍼지 다항식 뉴론(Fuzzy Polynomial Neuron : FPN)이라고 표현하다. 제안된 모델은 구조 선택에 있어서 유전자 알고리즘(Genetic Algorithms : GAs)을 이용하였다. 유전자 알고리즘을 사용하여 입력의 차수와 입력의 개수 그리고 후반부 추론의 형태를 최적 선택하였다. 비선형 데이터에 대한 모델 설계를 위해 최적화 알고리즘인 유전자 알고리즘 기반 FPNN 모델 설계가 유용하고 효과적임을 보인다.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.4
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1193-1202
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• 2000

The paper describes the use of genetic algorithms (GA's) to the minimum weight design of stiffened composite panels for buckling constraints. The proposed design problem is characterized by mixture of continuous and discrete design variables corresponding to panel elements and stacking sequence of laminates, respectively. Design space is multimodal and non-convex, thereby introducing the need for global search strategies. Advanced strategies in GA's such as directed crossover, multistage search and separated crossover are adopted to improve search ability and to save computational resource requirements. The paper explores the effectiveness of genetic algorithms and their advanced strategies in designing stiffened composite panels under various uniaxial compressive load conditions and the linrlit on stacking sequence of laminates.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.698-702
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• 1992

In this paper, Micro-Genetic algorithms(.mu.-GAs) is proposed on a minimum-time path planning for robot manipulator, which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can't often find the optimal values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimal values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.40-47
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• 1994

In this paper, Micro-Genetic algorithms($\mu$-GAs) is proposed on a minimum-time path planning for robot manipulator. which is a kind of optimization algorithm. The minimum-time path planning, which can allow the robot system to perform the demanded tasks with a minimum execution time, may be of consequence to improve the productivity. But most of the methods proposed till now suffers from a significant computation burden and can`t often find the optimaul values. One way to overcome such difficulties is to apply the Micro-Genetic Algorithms, which can allow to find the optimul values, to the minimum-time problem. This paper propose an approach for solving the minimum-time path planning by using Micro-Genetic Algorithms. The effectiveness of the proposed method is demonstrated using the 2 d.o.f plannar Robot manipulator.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.191-194
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• 2002

In this paper, a new GA-based methodology with information granules is suggested for construction of the fuzzy classifier. We deal with the selection of the fuzzy region as well as two major classification problems-the feature selection and the pattern classification. The proposed method consists of three steps: the selection of the fuzzy region, the construction of the fuzzy sets, and the tuning of the fuzzy rules. The genetic algorithms (GAs) are applied to the development of the information granules so as to decide the satisfactory fuzzy regions. Finally, the GAs are also applied to the tuning procedure of the fuzzy rules in terms of the management of the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example-the classification of the Iris data, is provided.

• Structural Engineering and Mechanics
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• v.71 no.5
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• pp.503-513
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• 2019

Genetic Algorithms (GAs) have found the best design for reinforced concrete frames. The design of the optimum beam sections by GAs has been unified. The process of the optimum-design sections has satisfied axial, flexural, shear and torsion necessities based on the designing code. The frames' function has contained the function of both concrete and reinforced steel besides the function of the frames' formwork. The results have revealed that limiting the dimension of frame-beam with the dimension of frame-column have increased the optimum function of the structure, thereby reducing the reanalysis requirement for checking the optimum-designed structures through GAs.