Improved Self-Adaptive Genetic Algorithm and its Application on the Dynamic Stochastic Shortest Path Problem

In reality transportation network is dynamic and stochastic. Studies on the dynamic stochastic shortest path problem are of great research and application value. This paper introduces basic genetic algorithm into it. Then an improved self-adaptive genetic algorithm is proposed by improving population initialization, selection strategy, crossover strategy and mutation strategy. In addition, the improved genetic algorithm adjusts the crossover and mutation factors adaptively. The results of simulation experiment show that the improved genetic algorithm proposed by this paper has much higher capacity of global optimization than Dijkstra and A* algorithm in the dynamic stochastic shortest path problem.