• 한국정밀공학회지
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• 제13권11호
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• pp.82-91
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• 1996

In this study, we put our object to carry out adaptive modeling of cutting process in turning system, and to find out the optimal cutting conditions to maximize material removal rate under some constraints. We used a back-propagation neural network to model the cutting process adaptively and a genetic algorithm to find out optimal cutting conditions. The experimental results show that a back-propagation neural network could model the cutting process effciently, and optimized cutting conditions for maximizing the material removal rate were obtained through the adaptive process model and genetic algorithms. Therefore, the proposed approach can be applied to the real machining system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.366-369
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• 2006

본 논문에서는 계층적 공정 경쟁 유전자 알고리즘을 통한 비선형시스템의 정보입자 기반 퍼지집합 퍼지집합 모델의 최적화 방법을 제안한다. 퍼지집합 모델은 주로 전문가의 경험에 기반을 두어 얻어지기 때문에 동정과 최적화 과정이 필요하며 GAs를 이용하여 퍼지모델을 최적화한 연구가 많이 있다. GAs는 전역 해를 찾을 수 있는 최적화 알고리즘으로 잘 알려져 있지만 조기 수렴 문제를 포함하고 있다. 병렬유전자 알고리즘(PGA)은 조기수렴를 더디게 하고 전역 해를 찾기 위한 진화알고리즘이다. 적응형 계층적 공정 경쟁기반 유전자 알고리즘(AHFCGA)을 이용하여 퍼지모델의 입력변수, 멤버쉽함수의 수, 멤버쉽함수의 정점 등의 전반부 구조와 파라미터를 동정하였고, LSE를 사용하여 후반부 파라미터를 동정하였으며 실험적 예제를 통하여 제안된 방법의 성능을 평가한다.

• 산업기술연구
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• 제19권
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• pp.209-216
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• 1999

In this paper, we proposed a modofied genetic algorithm to minimize misclassification rate for determining the codebook. Genetic algorithms are adaptive methods which may be used solve search and optimization problems based on the genetic processes of biological organisms. But they generally require a large amount of computation efforts. GAVQ can choose the optimal individuals by genetic operators. The position of individuals are optimized to improve the recognition rate. The technical properties of this study is that prevents us from the local minimum problem, which is not avoidable by conventional VQ algorithms. We compared the simulation result with Matlab using phoneme data. The simulation results show that the recognition rate from GAVQ is improved by comparing the conventional VQ algorithms.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2008년도 추계 공동 국제학술대회
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• pp.463-467
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• 2008

Heuristic optimization using hybrid algorithms have provided a robust and efficient approach for solving many optimization problems. In this paper, a new hybrid algorithm using adaptive genetic algorithm (aGA) and particle swarm optimization (PSO) is proposed. The proposed hybrid algorithm is applied to solve numerical optimization functions. The results are compared with those of GA and other conventional PSOs. Finally, the proposed hybrid algorithm outperforms others.

• 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.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.147-152
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• 2000

In this paper we propose a Genetic Algorithm for the path planning of an autonomous mobile robot. Genetic Algorithms(GAs) have advantages of the adaptivity such as GAs work even if an environment is time-varying or unknown. Therefore, we propose the path planning algorithms using the GAs-based approach and show more adaptive and optimal performance by simulation.

• 한국정밀공학회지
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• 제12권9호
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• pp.95-103
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• 1995

Recently the trends toward reduction in size of industrial products have increased the application of micro drilling. But micro drilling has still much difficulty so that the needs for active control which give adaptation to controller are expanding. In this paper initial cutting condition was determined for some sorkpieces by experiment and GA-based Fuzzy controller was devised by genetic algorithms and fuzzy inference. The fuzzy inference has been applied to the various prob- lems. However the determination of the membership function is one of the difficult problem. So we introduce a genetic algorithms and propose a self-tuning method of fuzzy membership function. Based on this intelligent control, automation of micro drilling was carried out like the cutting process of skilled machinist.

• 한국정밀공학회지
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• 제13권11호
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• pp.73-81
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• 1996

This paper presents a general procedure for the selection of the machining parameters for a given machine which provides the maximum material removal rate using a Genetic Algorithms(GAs). Some constraints were given in order to achieve desired surface integrity and cutting tool life conditions as wel as to protect machine tool. Such a constrained problem can be transformaed to unconstrained problem by associating a penalty with all constraint violations and the penalties are included in the function evaluation. Genetic Algorithms can be used for finding global optimum cutting conditions with respect to the above cost function transformed by pennalty function method. From the demonstration of the numerical results, it was found that the near optimal conditions could be obtained regardless of complex solution space such as cutting environment.

• 융합신호처리학회논문지
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• 제13권4호
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• pp.207-210
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• 2012

유전 알고리즘은 확률에 기반한 매우 효과적인 최적화 기법이지만 지역해로의 조기수렴과 전역해로의 수렴 속도가 느리다는 단점이 있다. 본 논문에서는 이러한 단점을 보완하기 위해 적응 분할법에 기반한 유전 알고리즘을 제안하였다. 유전 알고리즘이 전역해를 효과적으로 찾도록 하는 적응 분할법은 최적화의 복잡도를 줄이기 위해 탐색공간을 적응적으로 분할한다. 이러한 적응 분할법은 탐색공간의 복잡도가 증가할수록 더 효과적이다. 제안된 방법을 테스트 함수의 최적화 및 도립진자 제어를 위한 퍼지 제어기 설계 최적화에 적용하여 그 유효성을 보였다.

• 산업경영시스템학회지
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• 제18권36호
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• pp.133-141
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• 1995

One of the problems brought up in the effective execution of genetic algorithms is that if they come under any influences according as the population size is large or small. In the case of small population size the opportunities of premature convergence are increased when the greatly powerful or no good individual is generated during search of the solution space. And searching the solution space in the case of large population size, the difficulties under the execution cause to searching all for one by one individual in every generation applied is limited, this gives the many interruptions to the convergence of final solution. Now this paper gives a suggestion to set up the adaptive population size which could compute the more correct solution and simplify the development of computation performance.