Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization

Tobias Glasmachers

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arXiv · 2412.15647

Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization

Tobias Glasmachers

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Abstract

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.

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Tobias Glasmachers. 2024-12-20. Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization. https://arxiv.org/abs/2412.15647

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