用遗传算法思想提升生成模型测试时性能
Neural Genetic Search in Discrete Spaces
- 用训练好的生成模型实现父母条件生成的交叉操作
- 在路径规划、提示词攻击、分子设计中均表现优异
- 适合需要高效搜索的生成模型部署场景
高效的搜索方法对提升深度生成模型在测试阶段的性能至关重要。本文提出一种新的测试时搜索方法——神经遗传搜索（Neural Genetic Search, NGS），将遗传算法的进化机制融入深度模型的生成过程。NGS的核心思想是其交叉操作，定义为基于父代条件的生成，利用训练好的生成模型实现。该方法具有高度灵活性且易于实现，适用于多种深度生成模型。我们在三个不同领域验证了NGS的有效性和通用性：路由问题、语言模型的对抗性提示生成以及分子设计。
原文摘要 · Abstract (English)
Effective search methods are crucial for improving the performance of deep generative models at test time. In this paper, we introduce a novel test-time search method, Neural Genetic Search (NGS), which incorporates the evolutionary mechanism of genetic algorithms into the generation procedure of deep models. The core idea behind NGS is its crossover, which is defined as parent-conditioned generation using trained generative models. This approach offers a versatile and easy-to-implement search algorithm for deep generative models. We demonstrate the effectiveness and flexibility of NGS through experiments across three distinct domains: routing problems, adversarial prompt generation for language models, and molecular design.
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