Paper ID: 2412.15647 • Published Dec 20, 2024
Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization
Tobias Glasmachers
TL;DR
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We design a class of variable metric evolution strategies well suited for
high-dimensional problems. We target problems with many variables, not
(necessarily) with many objectives. The construction combines two independent
developments: efficient algorithms for scaling covariance matrix adaptation to
high dimensions, and evolution strategies for multi-objective optimization. In
order to design a specific instance of the class we first develop a (1+1)
version of the limited memory matrix adaptation evolution strategy and then use
an established standard construction to turn a population thereof into a
state-of-the-art multi-objective optimizer with indicator-based selection. The
method compares favorably to adaptation of the full covariance matrix.