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

• URL: http://arxiv.org/abs/2412.15647v1
• Date: Fri, 20 Dec 2024 08:05:42 GMT
• Title: Variable Metric Evolution Strategies for High-dimensional Multi-Objective Optimization
• Authors: Tobias Glasmachers,
• Abstract summary: We design a class of variable metric evolution strategies well suited for high-dimensional problems.<n>We target problems with many variables, not (necessarily) with many objectives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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