Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization

• URL: http://arxiv.org/abs/2304.03995v1
• Date: Sat, 8 Apr 2023 12:14:15 GMT
• Title: Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization
• Authors: Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag
• Abstract summary: We discover entirely new genetic algorithms in a data-driven fashion. We parametrize selection and mutation rate adaptation as cross- and self-attention modules. The learned algorithm can be applied to previously unseen optimization problems, search dimensions & evaluation budgets.
• Score: 13.131971623143622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpose tool for optimization, their particular instantiations can be heuristic and motivated by loose biological intuition. In this work we explore a fundamentally different approach: Given a sufficiently flexible parametrization of the genetic operators, we discover entirely new genetic algorithms in a data-driven fashion. More specifically, we parametrize selection and mutation rate adaptation as cross- and self-attention modules and use Meta-Black-Box-Optimization to evolve their parameters on a set of diverse optimization tasks. The resulting Learned Genetic Algorithm outperforms state-of-the-art adaptive baseline genetic algorithms and generalizes far beyond its meta-training settings. The learned algorithm can be applied to previously unseen optimization problems, search dimensions & evaluation budgets. We conduct extensive analysis of the discovered operators and provide ablation experiments, which highlight the benefits of flexible module parametrization and the ability to transfer (`plug-in') the learned operators to conventional genetic algorithms.

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