We present a stochastic, finite population model of genetic algorithms in dynamic environments. In this model, fitness functions alternate stochastically over time. The limit behavior of these systems can beutilized to express predictions of expected behavior and measurements of performance for the algorithm and its parameter choices. We provide methods to analyze and study the limit behavior and performance measures for these systems. We also show how the model and its predictions relate to a previously studied model with deterministically alternating fitness functions.
|Title of host publication||2003 Congress of Evolutionary Computation (CEC 2003), Australia, Canberra|
|Publication status||Published - 2003|