
[full paper] 
Shao Xiaowei
In this paper, a hybrid genetic algorithm called ant colony genetic algorithm is presented. The initial population of the method is generated from the each subspace which is divided from the feasible solution space of the optimization problem evenly. And every subspace is marked by initial pheromone. During the genetic operation, selection operator is under the effect of subspace’s pheromone remaining. Because of the initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space once to located good points for further exploration in subsequent operator. In addition, the effect of pheromone of each subspace in the selection can improve the speed of convergence rate of algorithm.
Keywords: Hybrid Genetic Algorithm, Ant Colony System, pheromone remaining
Citation: Shao Xiaowei: An Ant Colony Genetic Algorithm. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.11131114.