Applying Genetic Algorithm for Exploring the Effective Path in Network with Dead Ends (막힌 부분을 갖는 네트워크 내 효과적인 경로 탐색을 위한 유전 알고리즘 적용)

KIM JUNWOO (김준우); Min-Jung Lee (이민정)

Applying Genetic Algorithm for Exploring the Effective Path in Network with Dead Ends

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2013, 8(1), pp.55-62
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Published : February 28, 2013

KIM JUNWOO ¹, Min-Jung Lee ²

¹동아대학교
²세종사이버대학교

Accredited

ABSTRACT

The genetic algorithms that mimic the mechanism of the natural evolution is very useful tool for exploring the solutionsof the NP-hard problems such as a variety of combinatorial optimization problems. This paper aims to apply the genetic algorithm to solve the shortest path problem, and especially, the network with few feasible paths is considered. The proposed algorithm identifies the relevant nodes at first, and represents a solution by specifying their adjacent nodes to visit next. Moreover, any additional repairing procedure is considered. This simple encoding can make the population to include infeasible paths or paths with cycles, however, such paths can be deal with by the properly designed fitness function. Consequently, it is expected that the proposed genetic algorithm will be useful to explore the effective feasible paths in the networks with cycles or dead-end nodes, especially. The result of the experiment with the missionaries and cannibals problem, a well-known river-crossing problem, reveals that a path between two nodes in a network of which entire structure is hard to be analyzed can be obtained effectively by the proposed genetic algorithm.

KEYWORDS

Shortest Path Problem, Maze Passing Problem, Combinatorial Optimization, Genetic Algorithm, Missionaries and Cannibals Problem

Citation status

* References for papers published after 2023 are currently being built.

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89