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OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution
[article]
2023
arXiv
pre-print
Black-box optimization (BBO) algorithms are concerned with finding the best solutions for problems with missing analytical details. Most classical methods for such problems are based on strong and fixed a priori assumptions, such as Gaussianity. However, the complex real-world problems, especially when the global optimum is desired, could be very far from the a priori assumptions because of their diversities, causing unexpected obstacles. In this study, we propose a generative adversarial
arXiv:2102.03888v6
fatcat:i2af3corjfe4fdu33r56wvqtty