Machines are omnipresent. They produce, they transport. Machines facilitate work and assist. The increasing penetration of mechanical engineering by information technology enables considerable benefits. This circumstance is expressed by the term mechatronics, which means the close interaction of mechanics, electronics, control engineering and software engineering to improve the behavior of a technical system. The progressive integration of information technology will enable mechatronic systems with partial intelligence. We refer to such systems as self-optimizing systems. Self-optimizing systems have the ability to react autonomously and flexibly on changing operation conditions. The design of such systems is an even more interdisciplinary task than the design of conventional mechatronic systems. Additionally to mechanical, electrical, control and software engineers also experts from mathematical optimization and artificial intelligence are involved. As a consequence a domain-spanning methodology is necessary in order to guarantee an effective work flow between the participating developers from various domains and their domain-specific methods, terminologies and solutions. This contribution presents such a methodology. The main focus, however, lies on harnessing of experimental knowledge for the development of self-optimizing systems. This includes the generation and storage of once proven design solutions as well as a tool for the effective and domain-spanning reuse.

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