mlsanityData to 14 Oct 2026

Evolutionary Computation as Natural Generative AI

arXiv 2510.08590 ↗First posted 4 Oct 2025cs.NE cs.LGcited by 0

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Abstract (arXiv)

Generative AI (GenAI) has achieved remarkable success across a range of domains, but its capabilities remain constrained to statistical models of finite training sets and learning based on local gradient signals. This often results in artifacts that are more derivative than genuinely generative. In contrast, Evolutionary Computation (EC) offers a search-driven pathway to greater diversity and creativity, expanding generative capabilities by exploring uncharted solution spaces beyond the limits of available data. This work establishes a fundamental connection between EC and GenAI, redefining EC as Natural Generative AI (NatGenAI) -- a generative paradigm governed by exploratory search under natural selection. We demonstrate that classical EC with parent-centric operators mirrors conventional GenAI, while disruptive operators enable structured evolutionary leaps, often within just a few generations, to generate out-of-distribution artifacts. Moreover, the methods of evolutionary multitasking provide an unparalleled means of integrating disruptive EC (with cross-domain recombination of evolved features) and moderated selection mechanisms (allowing novel solutions to survive), thereby fostering sustained innovation. By reframing EC as NatGenAI, we emphasize structured disruption and selection pressure moderation as essential drivers of creativity. This perspective extends the generative paradigm beyond conventional boundaries and positions EC as crucial to advancing exploratory design, innovation, scientific discovery, and open-ended generation in the GenAI era.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 48 of its 108 references that are in the map. First posted on arXiv 4 Oct 2025.

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References · 48

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020Language Models are Few-Shot Learnerscited by 32,419
  5. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  6. 2023GPT-4 Technical Reportcited by 18,396
  7. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  9. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  10. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  11. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  12. 2017Deep reinforcement learning from human preferencescited by 3,912
36 more
  1. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  4. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  5. 2021Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigmcited by 606
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  7. 2022Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Modelscited by 382
  8. 2023BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusioncited by 282
  9. 2013Generalized Denoising Auto-Encoders as Generative Modelscited by 113
  10. 2017CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Normscited by 96
  11. 20233D-GPT: Procedural 3D Modeling with Large Language Modelscited by 95
  12. 2024LLaMA-Mesh: Unifying 3D Mesh Generation with Language Modelscited by 91
  13. 2022Language Models Can See: Plugging Visual Controls in Text Generationcited by 90
  14. 2022Diffusion Models Beat GANs on Topology Optimizationcited by 79
  15. 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
  16. 2021Hierarchical VAEs Know What They Don't Knowcited by 58
  17. 2018Evolutionary Generative Adversarial Networkscited by 42
  18. 2024Diffusion Models are Evolutionary Algorithmscited by 17
  19. 2019COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networkscited by 13
  20. 2021Half a Dozen Real-World Applications of Evolutionary Multitasking, and Morecited by 12
  21. 2015GSNs : Generative Stochastic Networkscited by 9
  22. 2023Prompt Evolution for Generative AI: A Classifier-Guided Approachcited by 9
  23. 2020CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Networkcited by 8
  24. 2020EvolGAN: Evolutionary Generative Adversarial Networkscited by 7
  25. 2024EmoDM: A Diffusion Model for Evolutionary Multi-objective Optimizationcited by 6
  26. 2024Heuristically Adaptive Diffusion-Model Evolutionary Strategycited by 6
  27. 2018Towards Distributed Coevolutionary GANscited by 5
  28. 2025AI-driven materials design: a mini-reviewcited by 5
  29. 2025Evolvable Conditional Diffusioncited by 5
  30. 2025LLM-to-Phy3D: Physically Conform Online 3D Object Generation with LLMscited by 4
  31. 2019Creative AI Through Evolutionary Computationcited by 3
  32. 2020A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimizationcited by 3
  33. 2023Identifiability of Product of Experts Modelscited by 3
  34. 2021Info-Evo: Using Information Geometry to Guide Evolutionary Program Learningcited by 2
  35. 2023UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translationcited by 1
  36. 2023Lecture Notes in Probabilistic Diffusion Modelscited by 1

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