@techreport{FischbachZaeffererStorketal., type = {Working Paper}, author = {Andreas Fischbach and Martin Zaefferer and J{\"o}rg Stork and Martina Friese and Thomas Bartz-Beielstein}, title = {From Real World Data to Test Functions}, url = {https://nbn-resolving.org/urn:nbn:de:hbz:832-cos4-4326}, pages = {24}, abstract = {When researchers and practitioners in the field of computational intelligence are confronted with real-world problems, the question arises which method is the best to apply. Nowadays, there are several, well established test suites and well known artificial benchmark functions available. However, relevance and applicability of these methods to real-world problems remains an open question in many situations. Furthermore, the generalizability of these methods cannot be taken for granted. This paper describes a data-driven approach for the generation of test instances, which is based on real-world data. The test instance generation uses data-preprocessing, feature extraction, modeling, and parameterization. We apply this methodology on a classical design of experiment real-world project and generate test instances for benchmarking, e.g. design methods, surrogate techniques, and optimization algorithms. While most available results of methods applied on real-world problems lack availability of the data for comparison, our future goal is to create a toolbox covering multiple data sets of real-world projects to provide a test function generator to the research community.}, language = {en} }