This paper aims at generating as well as extracting design strategies for a real world problem using an evolutionary learning classifier system. Data mining for a design optimization result as a virtual database specifies design information and discovers latent design knowledge. It is essential for decision making in real world problems. Although we employed several methods from classic statistics to artificial intelligence to obtain design information from optimization results, we may not cognize anything beyond a prepared database. In this study, we have applied an evolutionary learning classifier system as a data mining technique to a real world engineering problem. Consequently, not only it extracted known design information but also it successfully generated design strategies not to extract from the database. The generated design rules do not physically become innovative knowledge because the prepared dataset include Pareto solutions owing to complete exploration to the edge of the feasible region in the optimization. However, this problem is independent of the method, our evolutionary learning classifier system is a useful method for incomplete datasets.
Chiba, K., & Nakata, M. (2017). From Extraction to Generation of Design Information -Paradigm Shift in Data Mining via Evolutionary Learning Classifier System. In Procedia Computer Science (Vol. 108, pp. 1662–1671). Elsevier B.V. https://doi.org/10.1016/j.procs.2017.05.233