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3. Evolution Through Large Models

4. Evolution through Large Models

5. Evolution Through Large Models

6. Towards Consistent Predictive Confidence through Fitted Ensembles

7. Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search

8. First return, then explore

9. Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods

10. Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

11. Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity

12. Learning to Continually Learn

13. Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures

14. Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

15. An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue

16. Evolvability ES: Scalable and Direct Optimization of Evolvability

17. Deep Neuroevolution of Recurrent and Discrete World Models

18. Go-Explore: a New Approach for Hard-Exploration Problems

19. Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions

20. VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution

21. Differentiable plasticity: training plastic neural networks with backpropagation

22. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

23. On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent

24. Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

25. ES Is More Than Just a Traditional Finite-Difference Approximator

26. Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients

27. Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

28. The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

29. Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks

30. Fitted Learning: Models with Awareness of their Limits

31. First return, then explore

32. A Proposed Infrastructure for Adding Online Interaction to Any Evolutionary Domain

33. Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation

34. Evolvability Is Inevitable: Increasing Evolvability Without the Pressure to Adapt

35. Exploring Promising Stepping Stones by Combining Novelty Search with Interactive Evolution

49. Searching for Quality Diversity When Diversity is Unaligned with Quality

50. HyperNEAT: The First Five Years

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