• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.9-14
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• 2003

SGA (Single Genetic Algorithm) is a heuristic global optimization method based on the natural characteristics and uses many populations and stochastic rules. Therefore SGA needs many function evaluations and takes much time for convergence. In order to solve the demerits of SGA, $\mu$GA(Micro-Genetic Algorithm) has recently been developed. In this study, $\mu$GA which have small populations and fast convergence rate, was applied to structural optimization with discrete or integer variables such as 3, 10 and 25 bar trusses. The optimized results of $\mu$GA were compared with those of SGA. Solutions of $\mu$GA for structural optimization were very similar or superior to those of SGA, and faster convergence rate was obtained. From the results of examples, it is found that $\mu$GA is a suitable and very efficient optimization algorithm for structural design.

• 대한안전경영과학회지
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• 제7권3호
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• pp.85-96
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• 2005

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.479-484
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• 1998

As technology in a computer hardware and software advances, efficient information retrieval from multimedia database gets highly demanded. Recently, it has been actively exploited to retrieve information based on the stored contents. However, most of the methods emphasize on the points which are far from human intuition or emotion. In order to overcome this shortcoming , this paper attempts to apply interactive genetic algorithm to content-based image retrieval. A preliminary result with subjective test shows the usefulness of this approach.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.13-18
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• 2010

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a novel approach to hybrid optimization algorithm called MGA in MPI (Message Passing Interface) environments. The proposed MGA combines the benefit of rapid convergence property of Mean Field Annealing and the effective genetic operations. This paper also proposes the efficient data structures for pattern related information.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.48-51
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• 2000

The traveling salesman problem with precedence relations (TSPPR) is harder than general traveling salesman problem. In this paper we propose an efficient genetic algorithm (GA) to solve the TSPPR. The key concept of the proposed genetic algorithm is a topological sort (TS). The results of numerical experiments show that the proposed GA approach produces an optimal solution for the TSPPR.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.1277-1280
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• 1998

We propose a structural learning method of MLP classifiers for a given application using PfSGA (parameter-free species genetic algorithm), which is a combining of species genetic algorithm(SGA) and parameter-free genetic algorithm(PfGA). experimental results show that PfSGA can reduce the learing time of SGA and has no influence of parameter values on structural learning. And we also convince that PfSGA is more efficient than the other methods in the aspect of misclassification ratio, learning rate, and complexity of MLP structure.

• 한국통신학회논문지
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• 제33권11A호
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• pp.1095-1104
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• 2008

다중 사용자 OFDM 시스템에서 제한된 자원을 효율적으로 사용하기 위해서는 부반송파와 비트의 할당은 중요한 역할을 한다. 하지만 부반송파와 비트의 할당문제는 비선형적 문제로 모든 경우의 수를 계산하여 최적의 값을 얻기에는 사실상 불가능하다. 본 논문에서는 비선형적 문제의 효율적인 자원 활용을 위해서 새로운 유전자 알고리즘을 사용하였다. 논문에서 제안된 알고리즘은 기존의 정형화된 유전자 알고리즘보다 다양한 조합을 참고하여 해를 찾게 된다. 따라서 수치적 시뮬레이션 결과들을 통해서 기존의 알고리즘들과 제안된 알고리즘을 비교해 볼 때, 제안한 알고리즘이 기존의 알고리즘들보다 뛰어난 성능을 보임을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 학술대회 논문집 전문대학교육위원
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• pp.212-215
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• 2006

This paper a new method which applies a genetic algorithm for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. In addition, the proposed method introduces a ultra efficient MMP tracking in a solar power generation system.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 추계학술대회 논문집 Vol.19
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• pp.394-395
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• 2006

This paper a new method which applies a genetic algorithm for determining which sectionalizing switch to poerate in order to solve the distribution system loss minimization re-configuration problem. In addition, the proposed method introduces a ultra efficient MMP tracking in a solar power generation system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 추계학술대회 논문집 전력기술부문
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• pp.75-77
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• 2006

This paper a new method which applies a genetic algorithm for determining which sectionalizing switch to poerate in order to solve the distribution system loss minimization re-configuration problem. In addition, the proposed method introduces a ultra efficient MMP tracking in a solar power generation system.