This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. Recent applications of BO over strings have been hindered by the need to map inputs into a smooth and unconstrained latent space. Learning this projection is computationally and data-intensive. Our approach instead builds a powerful Gaussian process surrogate model based on string kernels, naturally supporting variable length inputs, and performs efficient acquisition function maximization for spaces with syntactical constraints. Experiments demonstrate considerably improved optimization over existing approaches across a broad range of constraints, including the popular setting where syntax is governed by a context-free grammar.

本文提出一种基于贝叶斯优化（BO）的方法，直接处理原始字符串，并首次在BO循环中使用字符串核和遗传算法。该方法建立在基于字符串核的强大高斯过程替代模型上，并具有自然支持可变长度输入的能力，并针对在语法限制下进行有效的获得函数最大化。实验在广泛的约束范围内展示了比现有方法更为改进的优化性能，包括用上下文无关文法控制语法的流行设置。

BOSS: 基于字符串空间的贝叶斯优化