Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of the trained optimizers. Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population to eventually obtain PAPs with good generalization. Yet, when applied to a specific problem class, these approaches have a major limitation. They require practitioners to provide instance generators specially tailored to the problem class, which is often non-trivial to design. This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE), for binary optimization problems where decision variables take values of 0 or 1. The key innovation of DACE lies in its neural network-based domain-agnostic instance representation and generation mechanism that delimitates the need for domain-specific instance generators. The strong generality of DACE is validated across three real-world binary optimization problems: the complementary influence maximization problem (CIMP), the compiler arguments optimization problem (CAOP), and the contamination control problem (CCP). Given only a small set of training instances from these classes, DACE, without requiring any domain knowledge, constructs PAPs with better generalization performance than existing approaches on all three classes, despite their use of domain-specific instance generators.

翻译：从数据中训练优化器的核心目标是实现泛化。然而，有限的训练实例常常制约了已训练优化器的泛化能力。协同演化方法通过同时演化并行算法组合（PAP）和实例种群来解决这一挑战，最终获得具有良好泛化能力的PAP。然而，当应用于特定问题类别时，这些方法存在一个主要局限：它们要求实践者提供专门为该问题类别量身定制的实例生成器，而这类生成器的设计通常并非易事。本文提出了一种通用的、开箱即用的PAP构建方法，称为参数化搜索的领域无关协同演化（DACE），适用于决策变量取值为0或1的二元优化问题。DACE的关键创新在于其基于神经网络的领域无关实例表示与生成机制，从而消除了对领域特定实例生成器的需求。DACE的强大通用性在三个现实世界的二元优化问题上得到了验证：互补影响力最大化问题（CIMP）、编译器参数优化问题（CAOP）和污染控制问题（CCP）。仅给定来自这些类别的一小组训练实例，DACE在无需任何领域知识的情况下，所构建的PAP在所有三个类别上均展现出比现有方法更优的泛化性能，尽管现有方法使用了领域特定的实例生成器。