• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 춘계학술대회 논문집
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• pp.65-69
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• 2001

In this paper, consider the three cases of decide for appling point a general Simple Genetic Algorithm about heuristic method(Bottom and Left Sliding) at the piece auto-nesting on the row plate. The 1st case, about only using the Simple Genetic Algorithm. The 2nd case, applied the heuristic method to the genetic operating of the Simple Genetic Algorithm. The 3rd case, applied the heuristic method to the final result of the Simple Genetic Algorirhm. The estimation of final result were proceed to developed simulation program in this research.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.198-202
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• 2004

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a test function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

• 대한기계학회논문집A
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• 제29권6호
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• pp.835-841
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• 2005

This study proposes a new optimization algorithm which is combined with genetic algorithm and ANOM. This improved genetic algorithm is not only faster than the simple genetic algorithm, but also gives a more accurate solution. The optimizing ability and convergence rate of a new optimization algorithm is identified by using a evaluation function which have several local optimum and an optimum design of rocker arm shaft. The calculation results are compared with the simple genetic algorithm.

• 한국공간구조학회논문집
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• 제8권3호
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• pp.75-82
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• 2008

본 연구에서는 방향벡터(direction vector)를 이용한 지역 탐색법과 유전자 알고리즘을 결합한 새로운 알고리즘인 D-GA를 제안한다. 새로운 개체(individual)를 찾기 위한 방향벡터로는 진화과정 중에 습득되는 정보를 활용하기 위한 학습방향벡터(Loaming direction vector)와 진화와는 무관하게 한 개체의 주변을 탐색하는 랜덤방향벡터(random direction vector) 등 두 가지를 구성하였다. 그리고, 10 부재 트러스 설계 문제에 단순 유전자 알고리즘과 D-GA를 적용하여 최적화를 수행하였고, 그 결과를 비교 검토함으로써 단순 GA에 비하여 D-GA의 정확성 및 효율성이 향상되었음을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1283-1286
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• 2005

Multimodal optimization is one of the most interesting topics in evolutionary computational discipline. Simple genetic algorithm, a basic and good-performance genetic algorithm, shows bad performance on multimodal problems, taking long generation time to obtain the optimum, converging on the local extrema in early generation. In this paper, we propose a new genetic algorithm with two new genetic mutational operators, i.e. global and local mutation operators, and no genetic crossover. The proposed algorithm is similar to Simple GA and the two genetic operators are as simple as the conventional mutation. They just mutate the genes from left or right end of a chromosome till the randomly selected gene is replaced. In fact, two operators are identical with each other except for the direction where they are applied. Their roles of shaking the population (global searching) and fine tuning (local searching) make the diversity of the individuals being maintained through the entire generation. The proposed algorithm is, therefore, robust and powerful.

• 대한기계학회논문집A
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• 제26권2호
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• pp.406-414
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• 2002

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

• Steel and Composite Structures
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• 제25권4호
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• pp.473-484
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• 2017

In this study a seismic retrofit scheme for a building structure was presented using steel plate slit dampers. The energy dissipation capacity of the slit damper used in the retrofit was verified by cyclic loading test. Genetic algorithm was applied to find out the optimum locations of the slit dampers satisfying the target displacement. The seismic retrofit of the model structure using the slit dampers was compared with the retrofit with enlarging shear walls. A simple damper distribution method was proposed using the capacity spectrum method along with the damper distribution pattern proportional to the inter-story drifts. The validity of the simple story-wise damper distribution procedure was verified by comparing the results of genetic algorithm. It was observed that the capacity-spectrum method combined with the simple damper distribution pattern leaded to satisfactory story-wise distribution of dampers compatible with the optimum solution obtained from genetic algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2002년도 추계학술대회 논문집
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• pp.40-44
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• 2002

선박 설계시 최적화에 있어 종래에는 Random search Parametric study, Hook&Jeeves Method등이 사용되어져 왔으나 1960년대 Genetic algorithm이 소개되고 꾸준히 발전함과 함께 선박 설계에서도 Genetic algorithm이 사용되기 시작하였다. 본 논문에서는 이러한 Genetic algorithm 중 Simple Genetic algorithm(SGA), Micro Genetic algorithm(MGA), Threshold Genetic algorithm(TGA), Hybrid Genetic algorithm(HGA)을 선박 설계에 적용하여 그 성능을 비교 검토해 보았다. MGA는 계산 부담을 줄이기 위해 작은 개체로 효율적인 탐색을 하며, TGA는 local optimum에서 쉽게 벗어나게 할 수 있는 특징이 있다. HGA는 Hook&Jeeves Method를 Genetic algorithm과 병합되어 있다. 이를 바탕으로 본 논문에서 망간단괴 수송선의 경제성을 평가한다. 평가 방법은 연간 300만톤을 생산한다고 가정하여 연간 운송 용적을 동호제약으로 해서 최적화를 한 뒤, 이를 이용하여 몇가지 Case로 나누어서 초기 자본, 연간 비용, 20년간 총 비용을 계산하여 가장 경제적인 선박을 선택한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 가을 학술발표논문집 Vol.30 No.2 (1)
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• pp.769-771
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• 2003

현재 많은 조직에서 위험 분석을 통해 현재 자신들의 보안상의 문제점을 파악하고 그에 따른 대책을 적용하고 있다. 기존의 보안 대책을 평가하고 새로운 보안 대책을 적용하는 데 않은 어려움이 따르므로 본 연구에서는 Simple Genetic Algorithm을 이용하여 현재 조직의 상황에 적절한 보안 대책을 제시할 수 있는 방법을 연구하고자 한다.