  Evolutionary algorithms have been widely studied from a theoretical
perspective. In particular, the area of runtime analysis has contributed
significantly to a theoretical understanding and provided insights into the
working behaviour of these algorithms. We study how these insights into
evolutionary processes can be used for evolutionary art. We introduce the
notion of evolutionary image transition which transfers a given starting image
into a target image through an evolutionary process. Combining standard
mutation effects known from the optimization of the classical benchmark
function OneMax and different variants of random walks, we present ways of
performing evolutionary image transition with different artistic effects.
