A comparative study using genetic algorithms to deal with dynamic environments

One of the approaches used in Evolutionary Algorithms (EAs) for problems in which the environment changes from time to time is to use techniques that preserve the diversity in population. We have tested and compared several algorithms that try to keep the population as diverse as possible. One of those approaches applies a new biologically inspired genetic operator called transformation, previously used with success in static optimization problems. We tested two EAs using transformation and two other classical approaches: random immigrants and hypermutation. The comparative study was made using the dynamic 0/1 Knapsack optimization problem. Depending on the characteristics of the dynamic changes, the best results were obtained with transformation or with hypermutation.