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.

翻译：生成式人工智能（GenAI）在多个领域取得了显著成功，但其能力仍受限于有限训练集的统计模型以及基于局部梯度信号的学习方式。这通常导致生成结果更多是衍生性的而非真正创造性的。相比之下，进化计算（EC）提供了一条基于搜索的路径，能够实现更高的多样性和创造性，通过探索可用数据边界之外的未知解空间来扩展生成能力。本研究建立了EC与GenAI之间的根本联系，将EC重新定义为自然生成式人工智能（NatGenAI）——一种在自然选择机制下通过探索性搜索驱动的生成范式。我们证明：采用亲本中心算子的经典EC与传统GenAI具有对应关系，而破坏性算子则能实现结构化的进化跃迁（通常仅需数代）以生成分布外的新颖产物。此外，进化多任务处理方法为整合破坏性EC（通过跨域重组进化特征）与调节性选择机制（允许新颖解得以保留）提供了独特途径，从而促进持续创新。通过将EC重新定义为NatGenAI，我们强调结构化破坏与选择压力调节是创造力的核心驱动力。这一视角将生成范式扩展到传统边界之外，并将EC定位为推动GenAI时代探索性设计、创新、科学发现及开放式生成的关键技术。