mlsanityData to 14 Oct 2026

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

arXiv 1803.03453 ↗First posted 9 Mar 2018cs.NEcited by 78

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Abstract (arXiv)

Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Key facts. Cited by 78 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Mar 2018.

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Citing papers per year: 2018: 2; 2019: 0; 2020: 0; 2021: 8; 2022: 14; 2023: 13; 2024: 16; 2025: 12; 2026: 13.

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  3. 2018Reward learning from human preferences and demonstrations in Ataricited by 316
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  8. 2023Guiding Pretraining in Reinforcement Learning with Large Language Modelscited by 187
  9. 2021Alignment of Language Agentscited by 168
  10. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 129
  11. 2023Training Socially Aligned Language Models on Simulated Social Interactionscited by 78
  12. 2022On scientific understanding with artificial intelligencecited by 70
38 more
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  2. 2024Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistantcited by 38
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  4. 2024Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?cited by 31
  5. 2022Reward Gaming in Conditional Text Generationcited by 30
  6. 2021Human irrationality: both bad and good for reward inferencecited by 25
  7. 2022Covariance Matrix Adaptation MAP-Annealingcited by 25
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  10. 2024A social path to human-like artificial intelligencecited by 20
  11. 2024Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislationcited by 10
  12. 2021Definitions of intent suitable for algorithmscited by 8
  13. 2024Text2Robot: Evolutionary Robot Design from Text Descriptionscited by 8
  14. 2024From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Modelscited by 8
  15. 2025Enhancing Large Language Model Reasoning with Reward Models: An Analytical Surveycited by 8
  16. 2025Alignment, Agency and Autonomy in Frontier AI: A Systems Engineering Perspectivecited by 7
  17. 2021Understanding and Avoiding AI Failures: A Practical Guidecited by 6
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  20. 2023Evolving Populations of Diverse RL Agents with MAP-Elitescited by 5
  21. 2025Evolvable Conditional Diffusioncited by 5
  22. 2022Severe Damage Recovery in Evolving Soft Robots through Differentiable Programmingcited by 4
  23. 2022Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variablescited by 4
  24. 2025RIVAL: Reinforcement Learning with Iterative and Adversarial Optimization for Machine Translationcited by 4
  25. 2021Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativitycited by 3
  26. 2022Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Researchcited by 3
  27. 2023Machine Lovecited by 3
  28. 2022EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCscited by 2
  29. 2023CERN for AI: A Theoretical Framework for Autonomous Simulation-Based Artificial Intelligence Testing and Alignmentcited by 2
  30. 2024Quantifying stability of non-power-seeking in artificial agentscited by 2
  31. 2024Seizing the Means of Production: Exploring the Landscape of Crafting, Adapting and Navigating Generative AI Models in the Visual Artscited by 2
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  33. 2025What if Eye...? Computationally Recreating Vision Evolutioncited by 2
  34. 2021Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspringcited by 1
  35. 2022Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agentscited by 1
  36. 2024On Creativity and Open-Endednesscited by 1
  37. 2024AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generatorscited by 1
  38. 2025Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Modelscited by 1

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