Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review

Solving a dynamic optimization problem means that the obtained results depend explicitly on time as a parameter. There are two major branches in which dynamic optimization occurs: (i) in dynamic programming and optimal control, and (ii) in dynamic fitness landscapes and evolutionary computation. In both fields, solving such problems is established practice while at the same time special and advanced aspects are still subject of research. In this chapter, we intend to give a comparative study of the two branches of dynamic optimization. We review both problem settings, define them, and discuss approaches for and issues in solving them. The main focus here is to highlight the connections and parallels. In particular, we show that optimal control problems can be understood as dynamic fitness landscapes, where for linear systems this relationship can even be expressed analytically.