• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.43-54
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• 2004

This study analyzes characteristics and applicability of genetic algorithms and genetic operators to optimize highway alignments. Genetic algorithms, one of artificial intelligence techniques, are fast and efficient search algorithms for generating, evaluation and finding optimal highway alignment alternatives. The performance of genetic algorithms as an optimal search tool highly depends on genetic operators that are designed as a problem-specific. This study adopts low mutation operators(uniform mutation operator, straight mutation operator, non-uniform mutation operator whole non-uniform mutation operator) to explore whole search spaces, and four crossover operators(simple crossover operator, two-point crossover operator, arithmetic crossover operator, heuristic crossover operator) to exploit food characteristics of the best chromosome in previous generations. A case study and a sensitivity analysis have shown that the eight problem-specific operators developed for optimizing highway alignments enhance the search performance of genetic algorithms, and find good solutions(highway alignment alternatives). It has been also found that a mixed and well-combined use of mutation and crossover operators is very important to balance between pre-matured solutions when employing more crossover operators and more computation time when adopting more mutation operators.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.179-186
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• 2005

This paper describes a genetic algorithm and compares three crossover operators for Rural Postman Problem with Time Windows (RPPTW). The RPPTW which is a multiobjective optimization problem, is an extension of Rural Postman Problem(RPP) in which some service places (located at edge) require service time windows that consist of earliest time and latest time. Hence, RPM is a m띤tieect optimization Problem that has minimal routing cost being serviced within the given time at each service Place. To solve the RPPTW which is a multiobjective optimization problem, we obtain a Pareto-optimal set that the superiority of each objective can not be compared. This Paper performs experiments using three crossovers for 12 randomly generated test problems and compares the results. The crossovers using in this Paper are Partially Matched Exchange(PMX) Order Exchange(OX), and Modified Order Exchange(MOX) which is proposed in this paper. For each test problem, the results show the efficacy of MOX method for RPPTW.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.42-44
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• 1998

Sequential Ordering Problem(SOP)은 여러 개의 도시를 방문함에 있어 '어떤 도시를 다른 도시보다 먼저 방문해야 한다'는 선행제약이 있는 비대칭 순회 세일즈맨 문제(Traveling Salesman Problem)로서, 주어진 선행 제약을 만족하면서 모든 도시를 한번씩만 경유하는 가장 짧은 경로를 찾는 NP-Complete에 속하는 문제이다. 유전자 알고리즘은 SOP와 같은 조합 최적화문제에 대해 유용한 메타휴리스틱의 한가지이다. 본 논문에서는 SOP에 유전자 알고리즘을 적용할 때, 선행제약을 만족하는 해를 생성하는데 사용할 수 있는 선행관계유지 유전 연산자를 소개하고 이를 비교한다. 비교하는 유전 연산자는 선행관계유지 교차연산자, 선행관계유지 순서기반 교차연산자, 최대부분순서/임의삽입 연산자, 선행관계유지 간선재결합 연산자이다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.61-63
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• 2003

지난 10년 동안 유전 알고리즘은 어렵고 복잡한 다양한 문제들을 해결하기 위한 새로운 방법으로 인식되어왔다. 이러한 유전 알고리즘의 성능은 알고리즘 내에 구현되는 여러 연산자들에 좌우된다. 따라서 많은 연구자들이 새로운 연산자 개발에 관심을 가져 왔었다. 특히, 가장 널리 알려진 조합최적화 문제 중에 하나인 알려진 traveling salesman problem (TSP)의 경우 NP-hard문제로 분류되어 현재까지 이를 해결하기 위한 다양한 유전 연산자들이 개발되어 왔었다. 따라서 본 논문에서는 TSP 문제를 test problem로 이용하여 이를 해결하기 위한 새로운 유전 연산자 특히 교차 (Crossover Operator) 연산자들을 제안하고 기존의 다양한 연산자들과 비교를 통해서 성능을 입증한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.661-667
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• 2005

In this paper, we propose a new hybrid genetic algorithm for sequential ordering problem (SOP). In the proposed genetic algorithm, the Voronoi quantized crossover (VQX) is used as a crossover operator and the path-preserving 3-Opt is used as a local search heuristic. VQX is a crossotver operator that uses the epistasis information of given problem instance. Since it is a crossover proposed originally for the traveling salesman problem (TSP), its application to SOP requires considerable modification. In this study, we appropriately modify VQX for SOP, and develop three algorithms, required in the modified VQX, named Feasible solution Generation Algorithm, Precedence Cycle Decomposition Algorithm, and Genic Distance Assignment Method. The results of the tests on SOP instances obtained from TSPLIB and ZIB-MP-Testdata show that the proposed genetic algorithm outperforms other genetic algorithms in stability and solution quality.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.2 s.54
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• pp.1-9
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• 2007

The method of generating the artificial acceleration time histories for seismic analysis based on genetic algorithms is presented. For applying to the genetic algorithms, the frequencies are selected as the decision variables eventually to be genes. An arithmetic average crossover operator and an arithmetic ratio mutation operator are suggested in this study. These operators as well as the typical simple crossover operator are utilized in generating the artificial acceleration time histories corresponding to the specified design response spectrum. Also these generated artificial time histories are checked whether their outward features are to be coincident with the recorded earthquake motion or not. The features include envelope shape, correlation condition between 2 horizontal components of motion, and the relationship of max. acceleration, max. velocity and max. displacement of ground.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.189-203
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• 2002

In this paper we compare the performances of adaptive operators in genetic algorithm. For the adaptive operators, the crossover and mutation operators of genetic algorithm are considered. One fuzzy logic controller is developed in this paper and two heuristics is presented from conventional works for constructing the operators. The fuzzy logic controller and two conventional heuristics adaptively regulate the rates of the operators during genetic search process. All the algorithms are tested and analyzed in numerical examples. Finally, the best algorithm is recommended.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1363-1366
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• 2003

기존 연구에서 3차원 공간 위상 관계 연산자를 정의하는 방법은 2차원 공간 위상 관계 정의를 위해 주로 사용되는 공간 교차 방법을 단순히 3차원으로 확장해서 정의하는 방식이다. 그러나 기존의 방법에 의해 정의된 3차원 위상 관계 연산자로는 3차원 데이터베이스를 이용한 실제 응용시스템에서 고려되어야 하는 특수한 위상 관계 분석을 수행하기 어렵다. 따라서 이 논문에서는 실제 응용에서 적용 될 수 있는 특수한 3차원 위상 관계를 정의하기 위하여 x,y 좌표와 z좌표를 분리해서 고려하는 새로운 기법을 제안한다. 또한 제안한 방법을 이용하여 새로운 3차원 공간 위상 관계 연산자들을 정의하고 구체적인 알고리즘을 제시한다.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.82-89
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• 2000

Edge detection is the first step and very important step in image analysis. In this paper, proposed edge detection operators based on informations of edge types and it is different from other classical edge detection operators such as gradient and surface fitting operators. The first, we defined characteristics of edge types such as localization, thinness, length. The second, we defined valid edge types and ideal edge pixel positions in $3\times3$window based on edge characteristics of edge types. And we proposed edge detection algorithm and twelve windows based on valid edge types. In specially, proposed algorithm was shown better performence of edge detection than other operators such as gradient operator and the LoG(Laplacian of Gaussian) operator of zero crossings.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.209-213
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• 1998

Job-Shop Scheduling 문제는 전형적인 NP-hard 문제로서 효율적인 발견적 기법을 필요로 한다. 본 연구에서는 이 문제에 대한 유전알고리즘들의 성능을 비교 분석한다. 유전 알고리즘의 주요 구성 요소들로는 크게 선별, 교차, 돌연변이 등이 존재하는데, 특히 선별은 적자 생존의 자연 법칙에 기초하여, 환경에 대한 적응도에 의해 현 세대의 모집단으로부터 다음 세대에 생존할 개체를 선택하는 과정으로 해의 산출에 중요한 역할을 하는 부분이다. 기존의 많은 연구들이 유전 연산자인 교차, 돌연변이 방법들에 대한 성능 비교에 초점을 맞추었는데, 본 연구에서는 선별 과정에 초점을 맞추어 기존의 알려진 여러 선별 방법 들을Job-Shop Scheduling 문제에의 적용을 통해 비교 분석하고 새로운 선별 방법을 제안한다.