Evolutionary algorithms in dynamic environments
Evolutionary algorithms (EAs) are widely and often used for solving stationary optimization problems where the fitness landscape or objective function does not change during the course of computation. However, the environments of real world optimization problems may fluctuate or change sharply. If the optimization problem is dynamic, the goal is no longer to find the extrema, but to track their progression through the search space as closely as possible. All kinds of approaches that have been proposed to make EAs suitable for the dynamic environments are surveyed, such as increasing diversity, maintaining diversity, memory based approaches, multi-population approaches and so on.
The file attached to this record is the author's final peer reviewed version.
Citation : Wang, H., Wang, D., and Yang, S. (2007) Evolutionary algorithms in dynamic environments. Control and Decision, 22(2): pp.127-131, 137.
ISSN : 1001-0920
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes