• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 봄 학술발표논문집 Vol.30 No.1 (A)
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• pp.40-42
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• 2003

본 논문에서는 유전자 알고리즘(Genetic Algorithm: GA)의 새로운 병렬화 방법을 제안 하고 있다. 기존의 병렬 유전자 알고리즘(Parallel Genetic Algorithm: PGA)은 전체 개체집단을 부개체집단 (Subpopulation)으로 나누어 해의 가능 영역을 동시에 탐색하는 것이 일반적인 방법인데 반해. 본 논문에서 제안하는 병렬화 방법은 전체 해의 영역을 나누어 각각의 영역에서 독립된 개체집단들이 서로 다른 영역을 탐색하게 하는 방법이다. 이 방법은 두 가지 단계의 병렬 유전자 알고리즘으로 구성된다. 먼저 적응교배 연산자(Adaptive Crossover Operator: ACO)를 이용한 PGA를 통해 지역해에 인접한 범위들로 해의 영역을 나누고, 이렇게 나누어진 각각의 영역들에서 다시 병렬로 GA를 적용시켜 자세하게 탐색하는 방법이다. 첫 번째 수행되는 PGA 단계에서는 탐색 시간을 줄이고 두 번째 PGA 단계에서는 보다 자세한 탐색을 하기 위해 정밀도(Precision)의 조정을 유전자 알고리즘의 병렬화에 적용하였으며. 이를 통해 빠르고 자세한 탐색이 가능한 유전자 알고리즘의 병렬화 방법을 제안하고 있다.

• 산업경영시스템학회지
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• 제23권59호
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• pp.11-20
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• 2000

The Job Shop Scheduling Problem(JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on single genetic algorithm(SGA) and parallel genetic algorithm (PGA) to address JSSP. In this scheduling method, new genetic operator, generating method of initial population are developed and island model PGA are proposed. The scheduling method based on PGA are tested on standard benchmark JSSP. The results were compared with SGA and another GA-based scheduling method. The PGA search the better solution or improves average of solution in benchmark JSSP. Compared to traditional GA, the proposed approach yields significant improvement at a solution.

• 대한산업공학회지
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• 제25권4호
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• pp.490-499
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• 1999

Vehicle routing problem(VRP) is known to be NP-hard problem, and good heuristic algorithm needs to be developed. To develop a heuristic algorithm for the VRP, this study suggests a parallel genetic algorithm(PGA), which determines each vehicle route in order to minimize the transportation costs. The PGA developed in this study uses two dimensional array chromosomes, which rows represent each vehicle route. The PGA uses new genetic operators. New mutation operator is composed of internal and external operators. internal mutation swaps customer locations within a vehicle routing, and external mutation swaps customer locations between vehicles. Ten problems were solved using this algorithm and showed good results in a relatively short time.

• 정보처리학회논문지A
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• 제13A권1호
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• pp.53-56
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• 2006

유전 알고리즘은 자연 선택과 유전적 성질에 기반을 둔 알고리즘으로 기존 방법으로는 쉽게 해결할 수 없는 어려운 문제에서도 성공적으로 적용되었다. 기존의 유전 알고리즘은 해 집단이 큰 경우 시간이 많이 걸리는 문제점이 있다. 병렬 유전 알고리즘은 이러한 문제를 해결하기 위하여 제안된 기존의 유전 알고리즘의 확장이라 할 수 있다. 병렬 유전 알고리즘에서 중요한 요소는 이주와 유전 연산으로 이를 적절하게 설계함으로서 좋은 결과를 얻을 수 있다. 본 논문에서는 퍼지 논리를 이용하여 기존의 병렬 유전 알고리즘을 개선하고자 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• 한국지능시스템학회논문지
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• 제8권5호
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• pp.61-71
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• 1998

Genetic Algorithm(GA) is a method of approaching optimization problems by modeling and simulating the biological evolution. GA needs large time-consuming, so ti had better do on a parallel computer architecture. Our proposed system has a VLSI-oriented interconnection network, which is constructed from a viewpoint of fractal geometry, so that self-similarity is considered in its configuration. The approach to Parallel Genetic Algorithm(PGA) on our proposed system is explained, and then, we construct the classifier system such that the set of samples is classified into weveral classes based on the features of each sample. In the process of designing the classifier system, We have applied PGA to the Traveling Salesman Problem and classified the sample set in the Euclidean space into several categories with a measure of the distance.

• 경영과학
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• 제20권1호
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• pp.51-64
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• 2003

The JSSP (Job Shop Scheduling Problem) Is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on genetic algorithm to address JSSP. we design scheduling method based on SGA (Single Genetic Algorithm) and PGA (Parallel Genetic Algorithm). In the scheduling method, the representation, which encodes the job number, is made to be always feasible, initial population is generated through integrating representation and G&T algorithm, the new genetic operators and selection method are designed to better transmit the temporal relationships in the chromosome, and island model PGA are proposed. The scheduling method based on genetic algorithm are tested on five standard benchmark JSSPs. The results were compared with other proposed approaches. Compared to traditional genetic algorithm, the proposed approach yields significant improvement at a solution. The superior results indicate the successful Incorporation of generating method of initial population into the genetic operators.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.375-378
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• 2002

In this paper we propose parallel GA to optimize mutation rate and crossover rate using server-client model. The performance of GA depend on the good choice of crossover and mutation rates. Although many researcher has been study about the good choice, it is still unsolved problem. proposed GA optimize crossover and mutation rates trough evolving subpopulation. In virtue of the server-client model, these parameters can be evolved rapidly with relatively low-grade

• 전기학회논문지
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• 제57권9호
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• pp.1636-1641
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• 2008

In this study, we introduce a new approach to Parallel Genetic Algorithms (PGA) which combines AHFCGA with crossover operator. As to crossover operators, we use three types of the crossover operators such as modified simple crossover(MSX), arithmetic crossover(AX), and Unimodal Normal Distribution Crossover(UNDX) for real coding. The AHFC model is given as an extended and adaptive version of HFC for parameter optimization. The migration topology of AHFC is composed of sub-populations(demes), the admission threshold levels, and admission buffer for the deme of each threshold level through succesive evolution process. In particular, UNDX is mean-centric crossover operator using multiple parents, and generates offsprings obeying a normal distribution around the center of parents. By using test functions having multimodality and/or epistasis, which are commonly used in the study of function parameter optimization, Experimental results show that AHFCGA can produce more preferable output performance result when compared to HFCGA and RCGA.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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• pp.137-140
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• 1996

This paper proposes a unit commitment scheduling method based on Parallel Genetic Algorithm(PGA). Due to a variety of constraints to be satisfied, such as the minimum up and down time constraints, the search space of the UC problem is highly nonconvex. So, we used transputer which is one of the practical parallel processors. It can give us fastness and effectiveness features of the proposed method for solving the problem. To show the effectiveness of the PGA based unit commitment scheduling, we tested results for system of 5 units and we can get desirable results.