Testing the Intermediate Disturbance Hypothesis: Effect of Asynchronous Population Incorporation on Multi-Deme Evolutionary Algorithms

In P2P and volunteer computing environments, resources are not always available from the beginning to the end, getting incorporated into the experiment at any moment. Determining the best way of using these resources so that the exploration/exploitation balance is kept and used to its best effect is an important issue. The Intermediate Disturbance Hypothesis states that a moderate population disturbance (in any sense that could affect the population fitness) results in the maximum ecological diversity. In the line of this hypothesis, we will test the effect of incorporation of a second population in a two-population experiment. Experiments performed on two combinatorial optimization problems, MMDP and P-Peaks, show that the highest algorithmic effect is produced if it is done in the middle of the evolution of the first population; starting them at the same time or towards the end yields no improvement or an increase in the number of evaluations needed to reach a solution. This effect is explained in the paper, and ascribed to the intermediate disturbance produced by first-population immigrants in the second population.