Migrational GA that preserves solutions in non-static optimization problems

The genetic algorithm (GA) has been successfully introduced to solve various optimization problems. One of the characteristics of the GA is that, once it has converged, most of its population members are copies of the best individual, causing the GA to lose population diversity. This characteristic is a setback when we consider non-stationary problems in which the fitness functions vary with time. In this paper, we propose a migrational GA that stores past environmental solutions and retrieves them rapidly when that environment is re-activated, through probabilistic operation