  Genetic algorithm (GA) is a stochastic metaheuristic process consisting on
the evolution of a population of candidate solutions for a given optimization
problem. By extension, multipopulation genetic algorithm (MPGA) aims for
efficiency by evolving many populations, or islands, in parallel and performing
migrations between them periodically. The connectivity between islands
constrains the directions of migration and characterizes MPGA as a dynamic
process over a network. As such, predicting the evolution of the quality of the
solutions is a difficult challenge, implying in the waste of computer resources
and energy when the parameters are inadequate. By using models derived from
statistical mechanics, this work aims to estimate equations for the study of
dynamics in relation to the connectivity in MPGA. To illustrate the importance
of understanding MPGA, we show its application as an efficient alternative to
the thermalization phase of Metropolis-Hastings algorithm applied to the Ising
model.
