At work I had the following problem: given M tasks and N DSPs, what was the best way to assign tasks to DSPs? "Best" was defined as "minimizing the load of the most loaded DSP". There were different types of tasks, and various task types had various performance ramifications depending on where they were assigned, so I encoded the set of job-to-DSP assignments as a "DNA string" and then used a genetic algorithm to "breed" the best assignment string I could.
It worked fairly well (much better than my previous method, which was to evaluate every possible combination... on non-trivial problem sizes, it would have taken years to complete!), the only problem was that there was no way to tell if the optimal solution had been reached or not. You could only decide if the current "best effort" was good enough, or let it run longer to see if it could do better.
