• 대한원격탐사학회지
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• 제34권1호
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.159-165
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• 2002

진화연산에는 교배, 돌연변이, 경쟁, 선택이 있다. 이러한 과정 중에서 선택은 새로운 개체를 생산하지는 않지만, 모든 해중에서 최적의 해가 될만한 해는 선택하고, 그러지 않은 해는 버리는 판단의 역할을 한다. 따라서 아무리 좋은 해를 만들었다고 해도, 취사 선택을 잘못하면, 최적의 해를 찾지 못하거나, 또 많은 시간이 소요되게 된다. 따라서 본 논문에서는 stochastic한 성질을 갖고 있는 Tournament selection에 Local selection개념을 도입하여, 지역 해에서 벗어나 전역 해를 찾는데, 개선이 될 수 있도록 하였고 Fast Evolutionary Programming의 mutation과정을 개선하고, Genetic Algorithm의 연산자인 crossover와 mutation을 도입하여 Parallel search로 지역 해에서 벗어나 전역 해를 찾는 하이브리드 알고리즘을 제안하고자 한다.

• 대한안전경영과학회지
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• 제5권1호
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• pp.69-82
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• 2003

The sequence which minimizes overall utility work in car assembly lines reduces the cycle time, the number of utility workers, and the risk of conveyor stopping. This study suggests mathematical formulation of the sequencing problem to minimize overall utility work, and present a genetic algorithm which can provide a near optimal solution in real time. To apply a genetic algorithm to the sequencing problem in car assembly lines, the representation, selection methods, and genetic parameters are studied. Experiments are carried out to compare selection methods such as roullette wheel selection, tournament selection and ranking selection. Experimental results show that ranking selection method outperforms the others in solution quality, whereas tournament selection provides the best performance in computation time.

• 한국지능시스템학회논문지
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• 제12권4호
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• Industrial Engineering and Management Systems
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• 제10권2호
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• pp.161-169
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• 2011

In seeking further efficiency in production preparation, it is common to examine assembly sequences using digital manufacturing. The assembly sequences affect the product evaluation, so it is necessary to test several assembly sequences before actual production. However, because selection and testing of assembly sequences depends on the operator's personal experience and intuition, only a small number of assembly sequences are actually tested. Nevertheless, there is a systematic method for generating assembly sequences using a contact-related figure. However, the larger the number of parts, the larger the number of assembly sequences geometric becomes. The purpose of this study is to establish a systematic method of generating efficient assembly sequences regardless of the number of parts. To generate such assembly sequences selectively, a "Tournament Tree," which shows the structure of an assembly sequence, is formulated. Applying the method to assembly sequences of a water valve, good assembly sequences with the same structure as the Tournament Tree are identified. The structure of such a Tournament Tree tends to have fewer steps than the others. As a test, the structure is then applied for a drum cartridge with 38 parts. In all the assembly sequences generated from the contact-related figures, the best assembly sequence is generated by using the Tournament Tree.

• 한국시뮬레이션학회논문지
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• 제22권4호
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• pp.139-147
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• 2013

본 논문에서는 질량 분석 방법에 의하여 산출된 단백체 데이터(mass spectrometric proteomic data)의 분류 분석(classification analysis)을 위한 새로운 특성 선택(feature selection) 방법을 제안한다. 이 방법은 i)높은 상관관계를 가지는 중복된 특성을 효과적으로 제거하는 전처리 단계와 ii)토너먼트(tournament) 전략을 사용하여 최적 특성 부분집합(optimal feature subset)을 탐색해 내는 단계로 구성되어 있다. 제안되는 방법을 실제 암진단에 사용되는 공개된 혈액 단백체 데이터에 적용하였으며 널리 사용되는 타 방법과 비교할 때 우수한 성능과 균형된 특이도와 민감도를 달성함을 실증하였다.

• 산업경영시스템학회지
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• 제19권38호
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• pp.43-50
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• 1996

Evolution Programs, a form of Genetic Algorithms transformed from chromosome representation, are applied to the Timetabling of University which is one of the NP-hard problems. At the step of algorithms application, each class is established to be a specific category in feasible solution space. At. the same time, the exiting gene used in chromosome expression of Evolution Programs is modified to satisfy constraints effectively by transformation of gene which has multi-information. The new crossover method for fester operation in the Recombination attempted.. Roulette wheel selection and tournament selection are prepared.

• 한국콘텐츠학회논문지
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• 제11권6호
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• pp.59-66
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• 2011

자원 제약이 있는 프로젝트 스케줄링 문제는 자원의 양은 제한되어 있고 작업들 간에 선행조건이 있는 일정계획 문제로서 NP-hard 문제 중에 하나로 알려져 있다. 이러한 문제는 결정론적인 방법을 사용해서는 주어진 시간 내에 최적해를 구하기 어렵기 때문에 근사 최적해를 빠른 시간에 구할 수 있는 휴리스틱 방법을 이용한다. 본 논문에서는 자원 제약이 있는 프로젝트 스케줄링 문제를 효율적으로 해결할 수 있는 유전알고리즘을 소개한다. 제안한 유전알고리즘은 스키마 이론을 적용한 교차 연산자와 실세계 토너먼트 선택 전략을 이용하였다. 표준 문제에 실험한 결과는 제안한 알고리즘이 기존의 알고리즘 보다 우수함을 보여주었다.

• Structural Engineering and Mechanics
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• 제58권1호
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• pp.163-179
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• 2016

The Cuckoo search (CS) algorithm is a simple and efficient global optimization algorithm and it has been applied to figure out large range of real-world optimization problem. In this paper, a new formula is introduced to the discovering probability process to improve the convergence rate and the Tournament Selection Strategy is adopted to enhance global search ability of the certain algorithm. Then an approach for structural damage identification based on modified Cuckoo search (MCS) is presented. Meanwhile, we take frequency residual error and the modal assurance criterion (MAC) as indexes of damage detection in view of the crack damage, and the MCS algorithm is utilized to identifying the structural damage. A simply supported beam and a 31-bar truss are studied as numerical example to illustrate the correctness and efficiency of the propose method. Besides, a laboratory work is also conducted to further verification. Studies show that, the proposed method can judge the damage location and degree of structures more accurately than its counterpart even under measurement noise, which demonstrates the MCS algorithm has a higher damage diagnosis precision.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권3호
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• pp.133-144
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• 2000

Genetic Algorithm(GA) has been known as a method of solving large-scaled optimization problems with complex constraints in various applications. Since a major drawback of the GA is that it needs a long computation time, the hardware implementations of Genetic Algorithm Processors(GAP) are focused on in recent studies. In this paper, a hardware-oriented GA was proposed in order to save the hardware resources and to reduce the execution time of GAP. Based on steady-state model among continuos generation model, the proposed GA used modified tournament selection, as well as special survival condition, with replaced whenever the offspring's fitness is better than worse-fit parent's. The proposed algorithm shows more than 30% in convergence speed over the conventional algorithm in simulation. Finally, by employing the efficient pipeline parallelization and handshaking protocol in proposed GAP, above 30% of the computation speed-up can be achieved over survival-based GA which runs one million crossovers per second (1㎒), when device speed and size of application are taken into account on prototype. It would be used for high speed processing such of central processor of evolvable hardware, robot control and many optimization problems.