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Traditionally, a large number of pilot experiments need to be performed to optimize hot strip rolling processes. With the development of computer and automation sciences, it has become possible to develop data-driven models for computer-aided process optimization and design of new products. The optimal process design combined data-driven models and multi-objective optimization algorithm has been intensively investigated. Hung1) obtained the optimal production process parameters for a wire bonding of ultra-thin chip scale package based on hybrid methods of artificial intelligence in microelectronic field. Lestan et al.2) developed a system for predicting the properties of the deposited material by using the genetic algorithm for modeling the processing parameters, which had made the production of high-quality products successful. Brinksmeier et al.3) had established physical and/or empirical models by using artificial neural network and fuzzy theories for grinding processes, and developed the optimized processes by using the genetic algorithm. Winter et al.4) developed the optimal grinding process parameters by applying the singleobjective and multi-objective optimization, which could be High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels