I'm working on an optimization problem using genetic algorithm. To increase diversity of potential solutions I'm using multi-population approach: Instead of evolving one population I run 10 populations in parallel for some number of steps (let's say 10000). Then I mix elements (solutions) between populations and repeat the cycle.
Are there any known, beneficial strategies for mixing populations in such approach? Right now I combine all populations in one, and divide them back in totally random manner, but I feel that it might not be the best solution.