Summary of Evolution Transformer: In-context Evolutionary Optimization, by Robert Tjarko Lange et al.
Evolution Transformer: In-Context Evolutionary Optimization
by Robert Tjarko Lange, Yingtao Tian, Yujin Tang
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Evolution Transformer, a causal Transformer architecture that characterizes a family of Evolution Strategies. This model leverages data to discover powerful optimization principles via meta-optimization. Given a trajectory of evaluations and search distribution statistics, the Evolution Transformer outputs a performance-improving update to the search distribution. The architecture imposes inductive biases, ensuring the update is invariant to population member order within a generation and equivariant to search dimension order. The model is trained using Evolutionary Algorithm Distillation, a technique that optimizes sequence models using teacher algorithm trajectories. Results show strong in-context optimization performance and generalization capabilities to challenging neuroevolution tasks. The paper also proposes a technique for self-referentially training the Evolution Transformer, starting from random initialization and bootstrapping its own learning progress. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve optimization algorithms by using data to learn better optimization principles. They create a model called Evolution Transformer that can find good ways to update search distributions based on past performance. The model is trained using a special technique that helps it learn from examples of how other optimization algorithms work. The results show that this new approach works well and can even generalize to difficult tasks. The paper also shows how the model can train itself, starting from scratch, which could be useful for future applications. |
Keywords
» Artificial intelligence » Bootstrapping » Distillation » Generalization » Optimization » Transformer
