Investigations of Factors Affecting the Genetic Algorithm for Shortest Driving Time

Abstract

In this paper we investigate the influences on the genetic algorithm for the shortest driving time problem due to factors such as nodes on a map, the population size, the mutation rate, the crossover rate, and the converging rate. When the nodes on the map increase, more execution time is needed and much difference between the approximate solution and the exact solution appear on running genetic algorithms. Also, from the view point of the population initialization, restart type and reback type affect the precision of approximate solutions and the execution time. The characteristics of the factors we find in the paper provide us insight how to improve the genetic algorithm for the shortest driving time problem.