Discovering Black-Box Optimizers via Evolutionary Meta-Learning

Discovering Black-Box Optimizers via Evolutionary Meta-Learning

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What Has The Learned Evolution Strategy Discovered?

20 of 26

20 of 26

What Has The Learned Evolution Strategy Discovered?

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Discovering Black-Box Optimizers via Evolutionary Meta-Learning

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  1. 1 Intro
  2. 2 The Creation of Adam' - Michelangelo (ca. 1508 - 1512)
  3. 3 The Creation of AGI (by Adam) - ML Community
  4. 4 Envisioned excitement curve of this talk
  5. 5 What is Black-Box Optimization
  6. 6 How does an Evolution Strategy work?
  7. 7 Challenges for Modern Evolutionary Optimization?
  8. 8 What is the power of JAX for Evolutionary Optimization? Parallel/Accelerated Fitness Rollouts
  9. 9 evosax: Accelerated Evolutionary Optimization
  10. 10 Discovering New Algorithms via Meta-Learning
  11. 11 Discovering New Algorithms via Meta-Evolution
  12. 12 Why not use Meta-V instead of Meta-?
  13. 13 Discovering Evolutionary Optimizers (&)
  14. 14 White-Box Evolution Strategy: Gaussian Search
  15. 15 Learned Evolution Strategy (LES) Architecture
  16. 16 Meta-Training Details for LES Discovery BBOB Functions
  17. 17 Discovering LES: Meta-Training on Low-D BBOB
  18. 18 Evaluating LES: Brax Control Tasks
  19. 19 Scaling Meta-Distributions Improves LES Discovery
  20. 20 What Has The Learned Evolution Strategy Discovered?
  21. 21 Self-Referential Meta-Evolution of Learned ES
  22. 22 How does a Genetic Algorithm work?
  23. 23 Learned Genetic Algorithms (LGA) 9
  24. 24 LGA Generalizes to HPO-B & Neuroevolution Tasks
  25. 25 LGA Applies Adaptive Elitism & MR Adaptation
  26. 26 On Survivorship Bias & The Hardware Lottery (Hooker, 21)

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