An important class of global optimisation methods are evolutionary algorithms (EAs), which are inspired by the natural evolution of species and show excellent convergence rates. Here, a population of concurrent models are used, which evolve towards better models by stochastic processes. The simultaneous evaluation of the independent models enables a high level of parallelisation for these algorithms resulting in a significant reduction in computing time.