Fig. 1: Schematic flow of EVOLER.
From: Machine learning-enabled globally guaranteed evolutionary computation
a, Learning a low-rank representation of unknown problem space: (i) randomly sampling s columns/rows of unknown space \({{{\bf{F}}}}\in {{\mathbb{R}}}^{M\times N}\) (d = 2, s = 3; taking a 2D Schwefel function for example); (ii) reconstructing the low-rank representation space \(\hat{{{{\bf{F}}}}}\in {{\mathbb{R}}}^{M\times N}\) (s.t., subject to); (iii) identifying an attention subspace. b, Exploring the small attention subspace via evolutionary computation and finding the global optimum.
