• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.167-168
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• 2015

유전자 알고리즘은 문제 크기가 커짐에 따라 해집합이 폭발적으로 늘어나 최적해를 찾기 힘든 최적화 문제에 주로 적용되는 알고리즘으로, 최근에는 지리정보시스템(GIS)의 경로 최적화 문제, 게임에서의 길찾기, 인공지능에 많이 적용되고 있다. 마이크로 유전자 알고리즘은 일반 유전자 알고리즘에 비해 작은 크기의 모집단을 사용함으로써 알고리즘의 효율을 높일 수 있는 장점이 있다. 따라서, 본 논문에서는 무향 Rural Postman Problem 해법으로 마이크로 유전자 알고리즘의 적용 방법을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.181-188
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• 2007

Undirected Rural Postman Problem(URPP) is a problem that finds a shortest tour traversing the given arcs at least once in a given network. The URPP is one of the basic network problems used in solving the various real-world problems. And it is known as NP-Complete. URPP is an arc-oriented problem that the direction of a tour in an arc has to be considered. Hence, In URPP, it is difficult to use the algorithm for Traveling Salesman Problem (TSP), which is a node-oriented problem, directly. This paper proposes the decoding algorithm using graph transformation in the genetic algorithm for URPP. That is, you can find the entire tour traversing without considering the direction of arcs by transforming the arc-oriented graph into the node-oriented graph. This paper compares the performances of the proposed algorithm with an existing algorithm. In the simulation results, the proposed algorithm obtained better than the existing algorithm

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.709-717
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• 1997

For an undirected graph G=(V, E), the Rural Postman Problem (RPP) is a problem that finds a minimum cost tour that must pass edges in E'($\subseteq$ E) at least once. RPP, such as Traveling Salesman Problem (TSP), is known as an NP. Complete problem. In the previous study of RPP, he structure of the chromosome is constructed by E' and the direction of the edge. Hence, the larger the size of IE' I is, the larger the size of the chromosome and the size of the solution space are. In this paper, we transform the RPP into a Hamiltonian graph and use a genetic algorithm to solve the transformed problem using restructured chromosomes. In the simulations, we analyze our method and the previous study. From the simulation results, it is found that the results of the proposed method is better than those of the previous method and the proposed method also obtains the near optimal solution in earlier generations than the previous study.