This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations. As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions. But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research. That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers. Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
CITATION STYLE
Stork, J., Friese, M., Zaefferer, M., Bartz-beielstein, T., Fischbach, A., Breiderhoff, B., … Tušar, T. (2020). High-Performance Simulation-Based Optimization. (T. Bartz-Beielstein, B. Filipič, P. Korošec, & E.-G. Talbi, Eds.), High-Performance Simulation-Based Optimization (Vol. 833, pp. 225–244). Springer International Publishing. Retrieved from http://link.springer.com/10.1007/978-3-030-18764-4
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