• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• 한국CDE학회논문집
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• 제1권3호
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• pp.234-241
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• 1996

Recently, Genetic Algorithms(GAs), which consist of genetic operators named selection crossover and mutation, are widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GAs are very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GAs. So, they can be easily applicable to wide territory of design optimization problems. Also, virtue to multi-point search procedure, they have higher probability of convergence to global optimum compared with traditional techniques which take one-point search method. The introduction of basic theory on GAs, and the application examples in combination optimization of ten-member truss structure are presented in this paper.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.225-228
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• 2001

DNA computing has been applied to the problem of getting an optimal solution since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems. This paper presents DNA coding method for finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms (GA). GA searches effectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses a tool of calculation or Information store with DNA molecules and four-type bases denoted by the symbols of A(Ademine), C(Cytosine), G(Guanine) and T(Thymine). The same operators, selection, crossover, mutation, are applied to the both DNA coding algorithm and genetic algorithms. The results show that the DNA based algorithm performs better than GA.

• 환경영향평가
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• 제22권6호
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• pp.713-724
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• 2013

In terms of waste load allocation, inequality of waste load discharge must be considered as well as economic aspects such as minimization of waste load abatement. The inequality of waste load discharge between areas was calculated with Gini coefficient and was included as one of the objective functions of the multi-objective waste load allocation. In the past, multi-objective functions were usually weighted and then transformed into a single objective optimization problem. Recently, however, due to the difficulties of applying weighting factors, multi-objective genetic algorithms (GA) that require only one execution for optimization is being developed. This study analyzes multi-objective waste load allocation using NSGA-II-aJG that applies Pareto-dominance theory and it's adaptation of jumping gene. A sensitivity analysis was conducted for the parameters that have significant influence on the solution of multi-objective GA such as population size, crossover probability, mutation probability, length of chromosome, jumping gene probability. Among the five aforementioned parameters, mutation probability turned out to be the most sensitive parameter towards the objective function of minimization of waste load abatement. Spacing and maximum spread are indexes that show the distribution and range of optimum solution, and these two values were the optimum or near optimal values for the selected parameter values to minimize waste load abatement.

• 전자공학회논문지S
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• 제35S권7호
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• pp.6-13
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• 1998

To constoruct a multi-channel system like Optical Frequency Division Multiplexing (OFDM) with the vast amount of transmission capability, a frequency stabilization is essential technique for getting narrower channel spacing and for minimizing fluctuation of oscillation frquency of each channel. This paper proposes a novel multi-channel optical frequency stabilization scheme that uses wavelength crossover properties of a Arrayed Waveguide Grating(AWG). The proposed scheme includes an effective control algorithm that carries out frequency stabilization of all channels through a simple control circuit, simulaneously. And also, a design method of AWG is included which has higher resolutio in consideration of system loss.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.

• 디자인학연구
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• 제17권3호
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• pp.449-460
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• 2004

최근에 스포츠유틸리티 차량이 점차 대중화되고 있다. 이것은 과거의 단순한 승용차 중심의 자동차 소비구조에서 보다 다양화 된 생활양식에 따라 보다 전문화 된 성능의 차량에 대한 수요가 늘어난 이유에서 인 것으로 보인다. 이러한 수요변화는 주 5일 근무에 의한 여가시간의 증가와 생활수준 향상에 따른 레저활동의 요구 증가 등도 원인이지만, 차량 자체의 성능향상과 차종 다양화 역시 중요한 요인으로 작용하고 있다. 자동차 발전의 역사 초기부터 다양한 유형의 다목적 차량들이 개발되고 사용되어오고 여는데, 이들은 미국의 $\ulcorner$지프$\lrcorner$에서 발전된 차량이 주류를 이루고 있다. 이 차량들은 여러 메이커들을 통해 각각의 기능적 전문성을 살린 다양한 정통적 유형과 복합개념의 차량들로 개발되고 있다. 복합개념의 차량들은 전반적으로 소형화를 추구하는 반면, 정통적 형태의 4륜 구동차량들은 대형화 및 고성능화 되는 양방향의 경향이 동시에 나타나고 있다. 향후에 이러한 경향은 다양한 메이커와 차종에서 확대되어 나타나게 될 것으로 예측된다.

• 한국지능시스템학회논문지
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• 제11권6호
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• pp.538-542
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• 2001

DNA computing 은 Adleman 실험 이후에 많은 여러 가지 최적화 문제에 적용되어 왔다. DNA computing의 장점은 스트링의 길이가 가변적이고 4가지 염기를 이용하기 때문에 복잡한 문제에 전역 최적점을 찾는데 기존의 다른 방법보다는 효율적이라는것이다. 본 논문에서는 이진 스트링의 개체 지단 위에서 모의진화를 일으켜 효율적으로 최적 해를 탐색하는 GA(Genetic Algorithms)와 생체 분자와 DNA를 계산의 도구 및 정보 저장도구로 사용하여 A(Adenine). C(Cytosine), G(Guanine), T(Thymine)등의 4가지 염기를 사용하는 DNA 코딩방법을 이용하여multi-modal 함수의 전역 최적점을 탐색하는 문제에서의 각각의 성능을 조사하였다. Selection, crossover, mutation등의 GA연산자를 DNA를 코딩에 동일하게 적용하였으며 최적의 해를 탐색하는데 걸리는 시간과 찾아낸 최적해의 값을 평가한다.을 평가한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1671-1689
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• 2013

The key issue of block-based multi-focus image fusion is to determine the size of the sub-block because different sizes of the sub-block will lead to different fusion effects. To solve this problem, this paper presents a novel genetic algorithm (GA) based multi-focus image fusion method, in which the block size can be automatically found. In our method, the Sum-modified-Laplacian (SML) is selected as an evaluation criterion to measure the clarity of the image sub-block, and the edge information retention is employed to calculate the fitness of each individual. Then, through the selection, crossover and mutation procedures of the GA, we can obtain the optimal solution for the sub-block, which is finally used to fuse the images. Experimental results show that the proposed method outperforms the traditional methods, including the average, gradient pyramid, discrete wavelet transform (DWT), shift invariant DWT (SIDWT) and two existing GA-based methods in terms of both the visual subjective evaluation and the objective evaluation.