Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capabilities of LLMs with the strengths of evolutionary algorithms. In this work, we present EvoCAD, a method for generating computer-aided design (CAD) objects through their symbolic representations using vision language models and evolutionary optimization. Our method samples multiple CAD objects, which are then optimized using an evolutionary approach with vision language and reasoning language models. We assess our method using GPT-4V and GPT-4o, evaluating it on the CADPrompt benchmark dataset and comparing it to prior methods. Additionally, we introduce two new metrics based on topological properties defined by the Euler characteristic, which capture a form of semantic similarity between 3D objects. Our results demonstrate that EvoCAD outperforms previous approaches on multiple metrics, particularly in generating topologically correct objects, which can be efficiently evaluated using our two novel metrics that complement existing spatial metrics.

翻译：将大型语言模型与进化计算算法相结合，是一个极具前景的研究方向，它充分利用了LLMs卓越的生成能力和上下文学习优势，同时结合了进化算法的长处。在本工作中，我们提出了EvoCAD，一种利用视觉语言模型和进化优化，通过符号表示生成计算机辅助设计（CAD）对象的方法。我们的方法对多个CAD对象进行采样，然后使用结合了视觉语言与推理语言模型的进化方法对其进行优化。我们使用GPT-4V和GPT-4o评估了我们的方法，在CADPrompt基准数据集上进行了测试，并与先前的方法进行了比较。此外，我们引入了两个基于欧拉特性定义的拓扑性质的新指标，它们能够捕捉3D对象之间的一种语义相似性。我们的结果表明，EvoCAD在多项指标上优于先前的方法，特别是在生成拓扑结构正确的对象方面，这可以通过我们提出的两个新颖指标来高效评估，它们补充了现有的空间度量指标。