LLM2TEA: Agentic AI Designer Finds Innovative Objects with Generative Evolutionary Multitasking

Abstract: In this paper, we introduce LLM-driven MultiTask Evolutionary Algorithm (LLM2TEA), the first agentic AI designer within a generative evolutionary multitasking (GEM) framework that promotes the crossover and synergy of designs from multiple domains, leading to innovative solutions that transcend individual disciplines. Of particular interest is the discovery of objects that are not only innovative but also conform to the physical specifications of the real world in science and engineering. LLM2TEA comprises a LLM to initialize a population of genotypes (defined by text prompts) describing the objects of interest, a text-to-3D generative model to produce phenotypes from these prompts, a classifier to interpret the semantic representations of the objects, and a physics simulation model to assess their physical properties. We propose several novel LLM-based multitask evolutionary operators to guide the search toward the discovery of high-performing practical objects. Experimental results in conceptual design optimization validate the effectiveness of LLM2TEA, revealing from 97\% to 174\% improvement in the diversity of innovative objects compared to the present text-to-3D generative model baseline. In addition, more than 73\% of the generated designs have better physical performance than the top 1\% percentile of the designs generated in the baseline. Moreover, LLM2TEA generates designs that are not only aesthetically creative but also functional in real-world applications. Several of these designs have been successfully 3D-printed, emphasizing the proposed approach's capacity to transform AI-generated outputs into tangible physical objects. The designs produced by LLM2TEA meets practical requirements while showcasing creative and innovative features, underscoring its potential applications in complex design optimization and discovery.