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Finding Shortest Path with Learning Algorithms

Ayoub Bagheri, Mohammad-R Akbarzadeh-T, Mohammad-H Saraee


This paper presents an approach to the shortest path routing problem that uses one of the most popular learning algorithms. The Genetic Algorithm (GA) is one of the most powerful and successful method in stochastic search and optimization techniques based on the principles of the evolution theory. The crossover operation examines the current solutions in order to find better ones and the mutation operation introduces a new alternative route. The shortest path problem concentrates on finding the path with minimum distance, time or cost from a source node to the goal node. Routing decisions are based on constantly changing predictions of the weights. Finally we arrange some experiments to testify the efficiency of our method. In most of the experiments, the Genetic algorithms found the shortest path in a quick time and had good performance.


Crossover, Fitness Function, Genetic Algorithm, Learning algorithm, Mutation, Routing, Shortest Path

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