• 전자공학회논문지 IE
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• 제46권2호
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• pp.7-10
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• 2009

본 논문은 진화 하드웨어 시스템의 엔진으로 사용하기 위한 유전알고리즘의 하드웨어 구현 및 실험과 분석에 대한 연구이다. 진화 하드웨어는 응용에 따라 동작되어지는 환경에 적응하여 동적이면서 자동적으로 자기의 구조를 바꿀 수 있는 능력을 가진 하드웨어로써 재구성 가능한 하드웨어 부분과 유전알고리즘과 같은 진화 연산을 하는 부분으로 구성 되어 있다. 유전알고리즘은 실시간 응용 부분 등에 있어서 하드웨어로 구현하는 것이 속도 면에서 유리하다. 하드웨어로 처리하는 것이 병렬성, 파이프라인 처리, 그리고 함수 사용 부분 등에 있어 소프트웨어의 단점을 보완하여 이득이 있기 때문이다. 본 논문에서는 유전알고리즘을 하드웨어로 구현하여, 몇 가지 예제에 대하여 실험을 하고 실험 결과를 분석하여 그 구조가 유리함을 보였다.

• 전자공학회논문지 IE
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• 제45권4호
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• pp.27-32
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• 2008

본 논문은 진화 하드웨어 시스템에 적용하기 위해서 유전알고리즘을 하드웨어 기술언어를 사용하여 구현하였다. 진화 하드웨어는 응용에 따라 동작되어지는 환경에 적응하여 동적이면서 자동적으로 자기의 구조를 바꿀 수 있는 능력을 가진 하드웨어를 의미한다. 따라서 정확한 하드웨어 사양이 주어지지 않는 응용에 있어서도 동작을 수행할 수 됐다. 진화 하드웨어는 재구성 가능한 하드웨어 부분과 유전알고리즘과 같은 진화 연산을 하는 부분으로 구성되어 있다. 유전알고리즘을 소프트웨어로 구현하는 것 보다 실시간 응용 부분 등에 있어서 하드웨어로 유전알고리즘을 구현하는 것이 유리하다. 하드웨어로 처리하는 것이 병렬성, 파이프라인 처리, 그리고 함수 사용 부분 등에 있어 소프트웨어의 단점을 보완하여 속도 면에서 이득이 있기 때문이다. 논문에서는 진화 하드웨어를 임베디드 시스템으로 구현하기 위하여 유전알고리즘을 하드웨어로 구현하였고, 몇 가지 예제에 대하여 검증을 수행하였다.

• 한국정밀공학회지
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• 제20권8호
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• pp.135-143
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• 2003

This paper presents the impact minimization of a biped robot by using genetic algorithm. In case we want to accomplish the designed plan under the special environments, a robot will be required to have walking capability and patterns with legs, which are in a similar manner as the gaits of insects, dogs and human beings. In order to walk more effectively, studies of mobile robot movement are needed. To generate optimal motion for a biped robot, we employ genetic algorithm. Genetic algorithm is searching for technology that can look for solution from the whole district, and it is possible to search optimal solution from a fitness function that needs not to solve differential equation. In this paper, we generate trajectories of gait and trunk motion by using genetic algorithm. Using genetic algorithm not only on gait trajectory but also on trunk motion trajectory, we can obtain the smoothly stable motion of robot that has the least impact during the walk. All of the suggested motions of biped robot are investigated by simulations and verified through the real implementation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.275-280
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• 2009

A Genetic Algorithm (GA) is a kind of search techniques used to find exact or approximate solutions to optimization and searching problems. This paper discusses the design of a genetic algorithm-based intelligent parking system. This is a search strategy based on the model of evolution to solve the problem of parking systems. A genetic algorithm for an optimal solution is used to find a series of optimal angles of the moving vehicle at a parking space autonomously. This algorithm makes the planning simpler and the movement more effective. At last we present some simulation results.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2008년 영문 학술대회
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• pp.469-475
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• 2008

The design of a scramjet inlet is a process to search global optimization results among those factors influencing the geometry of scramjet in their ranges for some requirements. An optimization algorithm of hybrid genetic algorithm based on genetic algorithm and simplex algorithm was established for this purpose. With the sample provided by a uniform method, the compressive angles which also are wedge angles of the inlet were chosen as the inlet design variables, and the drag coefficient, total pressure recovery coefficient, pressure rising ratio and the combination of these three variables are designed specifically as different optimization objects. The contrasts of these four optimization results show that the hybrid genetic algorithm developed in this paper can capably implement the optimization process effectively for the inlet design and demonstrate some good adaptability.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.207-212
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• 2003

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.181-186
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• 2004

This paper proposed trajectory tracking control of Mobile Robot. Trajectory tracking control scheme are Real coding Genetic-Algorithm and Back-propergation Algorithm. Control scheme ability experience proposed simulation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.612-618
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• 1998

The success of a fuzzy logic control system solving any given problem critically depends on the architecture of th network. Various attempts have been made in optimizing its structure its structure using genetic algorithm automated designs. In a regular genetic algorithm , a difficulty exists which lies in the encoding of the problem by highly fit gene combinations of a fixed-length. This paper presents a new approach to structurally optimized designs of a fuzzy model. We use a messy genetic algorithm, whose main characteristics is the variable length of chromosomes. A messy genetic algorithms used to obtain structurally optimized fuzzy models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the exampled of a cart-pole balancing.

• Journal of Electrical Engineering and Technology
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• 제3권1호
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• pp.125-129
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• 2008

A new genetic algorithm for the solution of a multi-tasking problem is presented in this paper. The approach introduces innovative genetic operation that guides the genetic algorithm more directly towards better quality of the population. A wide variety of standard genetic parameters are explored, and results allow the comparison of performance for cases both with and without the new operator. The proposed algorithm improves the convergence speed by reducing the number of generations required to identify a near-optimal solution, significantly reducing the convergence time in each instance.

• International Journal of Control, Automation, and Systems
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• 제5권1호
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• pp.79-85
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• 2007

In this paper, a real-coded genetic algorithm is proposed for identification of time delay systems from step responses. FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) systems, which are the most useful processes in this field, but are difficult for system identification because of a long dead-time problem and a model mismatch problem. Genetic algorithms have been successfully applied to a variety of complex optimization problems where other techniques have often failed. Thus, the modified crossover operator of a real-code genetic algorithm is proposed to effectively search the system parameters. The proposed method, using a real-coding genetic algorithm, shows better performance characteristics when compared to the usual area-based identification method and the directed identification method that uses step responses.