The rapid research and development of generative artificial intelligence has
enabled the generation of high-quality images, text, and 3D models from text
prompts. This advancement impels an inquiry into whether these models can be
leveraged to create digital artifacts for both creative and engineering
applications. Drawing on innovative designs from other domains may be one
answer to this question, much like the historical practice of ``bionics", where
humans have sought inspiration from nature's exemplary designs. This raises the
intriguing possibility of using generative models to simultaneously tackle
design tasks across multiple domains, facilitating cross-domain learning and
resulting in a series of innovative design solutions. In this paper, we propose
LLM2FEA as the first attempt to discover novel designs in generative models by
transferring knowledge across multiple domains. By utilizing a multi-factorial
evolutionary algorithm (MFEA) to drive a large language model, LLM2FEA
integrates knowledge from various fields to generate prompts that guide the
generative model in discovering novel and practical objects. Experimental
results in the context of 3D aerodynamic design verify the discovery
capabilities of the proposed LLM2FEA. The designs generated by LLM2FEA not only
satisfy practicality requirements to a certain degree but also feature novel
and aesthetically pleasing shapes, demonstrating the potential applications of
LLM2FEA in discovery tasks.

本研究旨在通过多领域知识的传递，提出 LLM2FEA 作为首次尝试，以发现生成模型中的创新设计。实验结果在 3D 空气动力学设计方面验证了 LLM2FEA 的发现能力。LLM2FEA 生成的设计不仅在一定程度上满足实用性要求，而且具有新颖而美观的形状，展示了 LLM2FEA 在发现任务中的潜在应用。

LLM2FEA: 用生成进化多任务技术发现新颖设计

LLM2FEA: Discover Novel Designs with Generative Evolutionary  Multitasking

Synthesis of digital artifacts conditioned on user prompts has become an
important paradigm facilitating an explosion of use cases with generative AI.
However, such models often fail to connect the generated outputs and desired
target concepts/preferences implied by the prompts. Current research addressing
this limitation has largely focused on enhancing the prompts before output
generation or improving the model's performance up front. In contrast, this
paper conceptualizes prompt evolution, imparting evolutionary selection
pressure and variation during the generative process to produce multiple
outputs that satisfy the target concepts/preferences better. We propose a
multi-objective instantiation of this broader idea that uses a multi-label
image classifier-guided approach. The predicted labels from the classifiers
serve as multiple objectives to optimize, with the aim of producing diversified
images that meet user preferences. A novelty of our evolutionary algorithm is
that the pre-trained generative model gives us implicit mutation operations,
leveraging the model's stochastic generative capability to automate the
creation of Pareto-optimized images more faithful to user preferences.

本文提出了一种利用进化算法和多目标实现的新的方式，使用图像分类器引导产生的多样性生成的技术，来改善生成模型无法连接生成输出和期望目标概念 / 偏好的局限性，通过一种通过对生成进化过程规定选择压力和变异性的方式，产生多个更符合用户偏好的图像。