Using theory to self-tune migration periods in distributed genetic algorithms

In this paper we design a new distributed genetic algorithm, which is able to self-adapt the value of one of the most important parameter in this kind of techniques using the information provided by theoretical models. We study different alternative ways to use the mathematical results in our genetic algorithm. We test our technique on a wide set of instances of the well-known MAX-SAT problem. Experiments show that our self-* proposal is able to obtain similar, or even better, results when it is compared to traditional algorithms whose setting is made by hand. We also show the benefits in terms of saving time and complexity of migration policy settings for distributed genetic algorithms without reducing their efficiency.