Unconventional programming : programming non programmable systems Unconventional and natural computing research offers controlled information modification processes in uncommon media for example on the molecular scale or in bacteria colonies. Promising aspects of such systems are often the non linear behavior and the high connectivity of the involved information processing components in analogy to neurons in the nervous system. Unfortunately such properties make the system behavior hard to understand hard to predict and thus also hard to program with common engineering principles like modularization and composition leading to the term of non programmable systems. In contrast to many unconventional computing works that are often focused on finding novel computing substrates and potential applications unconventional programming approaches for such systems are the theme of this thesis: How can new programming concepts open up new perspectives for unconventional but hopefully also for traditional digital computing systems Mostly based on a model of artificial wet chemical neurons different unconventional programming approaches from evolutionary algorithms information theory self organization and self assembly are explored. A particular emphasis is given on the problem of symbol encodings: Often there are multiple or even an unlimited number of possibilities to encode information in the phase space of dynamical systems e.g. spike frequencies or population coding in neural networks. But different encodings will probably be differently useful dependent on the system properties the information transformation task and the desired connectivity to other systems. Hence methods are investigated that can evaluate analyse as well as identify suitable symbol encoding schemes.