Distributed genetic algorithms with randomized migration rate

This paper discusses the effect of randomization of migration rate in distributed genetic algorithms (DGAs). DGAs are extended al-gorithms of GAS that can be performed in parallel. In DGAs, the total population of genes is divided into subpopulations called is-lands. In each island, a simple GA is performed and some of the individuals in each island are moved to another island after a cer-tain migration interval. The number of genes in the DGA is deter-mined by the migration rate. Although DGAs can find optimum solutions even when there are several peaks in objective functions, they require more parameters than simple GAS and are therefore more time intensive. In this study, we describe a new DGA in which the migration rate is randomized (DGA/rmr). This algo-rithm is evaluated using two numerical simulations: the Rastrigin function and the Rosenbrock function. We show that optimal pa-rameters exist in these systems and obtain solutions with the pro-posed approach. The solutions are not optimal, but are better than those obtained using a DGA with fixed migration rate. DGA/rmr may therefore be a less time intensive alternative to conventional DGAs. 1