• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.704-711
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• 1999

This paper presents a design of fuzzy power system stabilizer (FPSS) using adaptive evolutionary computation (AEC). We have proposed an adaptive evolutionary algorithm which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary computations. FPSS shows better control performances than conventional power system stabilizer (CPSS) in three-phase fault with heavy load which is used when tuning FPSS. To show the robustness of the proposed FPSS, it is appliedto damp the low frequency oscillations caused by disturbances such as three-phase fault with normal and light load, the angle deviation of generator with normal and light load and the angle deviation of generator with heavy load. Proposed FPSS shows better robustness than CPSS.

• 한국인터넷방송통신학회논문지
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• 제13권6호
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• pp.147-154
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• 2013

LED 통신 환경에서 Adaptive Genetic Algorithm을 적용한 수신전력 변동 범위를 최적화하는 방안을 제안한다. 유전자 알고리즘을 이용해 고정 또는 이동 사용자를 위해 동적으로 전력 분배를 최적화함으로써 적응성, 환경 특성 및 사용자의 이동 패턴으로부터 독립하여 맞춤형 시스템 설계의 필요성을 없애므로 사용자의 편리성을 쉽게 높일 수 있다. 또한 실내 모든 위치에서 전력편차를 줄임으로써 에너지 절감 할 수 있다. 시뮬레이션을 실행한 결과, 제안방식은 장애물이 존재하지 않은 빈 방을 고려하였으며, 전력편차는 $10.5{\mu}W$가 감소하였으며, 약 10%의 수신 전력 편차를 감소시킬 수 있다는 것을 보여주고 있다. 또한 제안한 유전자 알고리즘이 기존의 방법과 비교하였을 때, 최적값으로의 수렴이 개선되어서 에너지 절감 측면에서 효율적임을 확인하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.179-182
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• 2003

This paper proposes an adaptive hybrid genetic algorithm (aHGA) for effectively solving the complex reliability optimization problems. The proposed aHGA uses a loca1 search technique and an adaptive scheme for respectively constructing hybrid algorithm and adaptive ability. For more various comparisons with the proposed adaptive algorithm, other aHGAs with conventional adaptive schemes are considered. These aHGAs are tested and analyzed using two complex reliability optimization problems. Numerical result shows that the proposed aHGA outperforms the other competing aHGAs.

• Computers and Concrete
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• 제2권5호
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• pp.411-421
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• 2005

A linear programming problem of the optimal proportioning of concrete aggregates is discussed; and a self-adaptive genetic algorithm is developed to solve this problem. The proposed method is based on changing a range of variables for capturing the feasible region of the optimum solution. A computational verification of this method is compared with the results of the linear programming.

• 지능정보연구
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• 제17권2호
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• pp.1-22
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• 2011

본 논문에서는 선행제약순서결정문제(Sequencing problem with precedence constraints, SPPC)를 효과적으로 해결하기 위한 적응형 유전알고리즘(Adaptive genetic algorithm, aGA)을 제안한다. aGA에서 는 SPPC를 효과적으로 표현하기 위해 위상정렬에 기초한 표현절차(topological sort-based representation procedure) 를 사용한다. 제안된 aGA는 퍼지로직제어를 이용한 적응형구조를 가지고 있으며, 유전 탐색과정을 통해 교차변이 연산자(Crossover operator)의 비율을 적응적으로 조절한다. 수치예제에서는 다양한 형태의 SPPC를 제시하였으며, 그 실험결과는 제안된 aGA가 기존의 알고리즘보다 우수함을 보여주었다. 결론적으로 말하자면 본 논문에서는 제안된 aGA가 다양한 형태의 SPPC에서 최적해 혹은 최적순서를 발견하는데 아주 효과적이라는 것을 밝혔다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2004년도 추계학술발표논문집(상)
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• pp.459-462
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• 2004

This paper proposes a genetically inspired adaptive clustering algorithm. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster purity. Chromosome encoding that ensures the correct number of clusters and cluster purity is discussed. The required fitness function is desisted on the basis of modified similarity criteria and genetic operators. These are incorporated into the proposed adaptive clustering algorithm. Experimental results show the efficiency of the clustering algorithm on synthetic data sets and real world data sets.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.502-504
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of FIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate FIR filter parameter using the genetic algorithm.

• 전기학회논문지
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• 제57권9호
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• pp.1636-1641
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• 2008

In this study, we introduce a new approach to Parallel Genetic Algorithms (PGA) which combines AHFCGA with crossover operator. As to crossover operators, we use three types of the crossover operators such as modified simple crossover(MSX), arithmetic crossover(AX), and Unimodal Normal Distribution Crossover(UNDX) for real coding. The AHFC model is given as an extended and adaptive version of HFC for parameter optimization. The migration topology of AHFC is composed of sub-populations(demes), the admission threshold levels, and admission buffer for the deme of each threshold level through succesive evolution process. In particular, UNDX is mean-centric crossover operator using multiple parents, and generates offsprings obeying a normal distribution around the center of parents. By using test functions having multimodality and/or epistasis, which are commonly used in the study of function parameter optimization, Experimental results show that AHFCGA can produce more preferable output performance result when compared to HFCGA and RCGA.

• 디지털산업정보학회논문지
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• 제17권2호
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• pp.13-20
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• 2021

Placement is a very important step in the VLSI physical design process. It is the problem of placing circuit modules to optimize the circuit performance and reliability of the circuit. It is used at the layout level to find strongly connected components that can be placed together in order to minimize the layout area and propagation delay. The most popular algorithms for circuit placement include the cluster growth, simulated annealing, integer linear programming and genetic algorithm. In this paper we propose a adapted genetic algorithm searching solution space for the placement problem, and then compare it with simulated annealing and genetic algorithm by analyzing the results of each implementation. As a result, it was found that the adaptive genetic algorithm approaches the optimal solution more effectively than the simulated annealing and genetic algorithm.