Distributed evolutionary algorithms with adaptive migration period
Karel Osorio, Gabriel Luque, Enrique Alba · 2011
In this work we use mathematical models, based on the study of the dynamics of the distributed evolutionary algorithms (dEA), to design self adaptive migration schedule for dEAs. We test our technique on two different problems: MAXSAT (a variant of the satisfiability problem), and a large scale problem, namely the radio network design problem. Its results are compared against the best results produced by distributed configurations with traditional tuning (constant preset migration schedules). Our experiments show that the technique produces results close to the best results obtained with fixed schedules while reducing the heavy cost of the parameter tuning.