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We have introduced a well-defined scalable benchmark problem - the eigenvalue placement problem - to investigate scalability issues in automated topology synthesis of mechatronic systems based on bond graphs and genetic programming. This classical inverse problem shares characteristics with many other system synthesis problems, such as electric circuit and controller synthesis, in terms of epistasis and multi-modality of the search space. Critical issues of open-ended topology search by genetic programming are investigated, including encoding, population seeding, scalability and evolvability. For the eigenvalue problems, we have found there exists a correlation between structure and function that is important for efficient topology search. Standard genetic programming has been used to solve up to 20-eigen-value problems, finding the target system of bush topology out of 823,065 possibilities with only 29506 topology evaluations.