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 novelty of DACE lies in its neural network-based domain-agnostic instance representation and generation mechanism that eliminates 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 problem classes, DACE, without requiring domain knowledge, constructs PAPs with even better generalization performance than existing approaches on all three classes, despite their use of domain-specific instance generators.

翻译：泛化能力是从数据中训练优化器的核心目标。然而，有限的训练实例常常制约着训练所得优化器的泛化性能。协同演化方法通过同时演化并行算法组合与实例种群来应对这一挑战，最终获得具备良好泛化能力的并行算法组合。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针对该问题类别设计的实例生成器，而这通常难以构建。本研究提出了一种通用、即插即用的并行算法组合构建方法，称为参数化搜索的领域无关协同演化，适用于决策变量取值为0或1的二元优化问题。该方法的核心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，该机制消除了对领域特定实例生成器的需求。本方法在三个实际二元优化问题上验证了其强大的泛化能力：互补影响力最大化问题、编译器参数优化问题以及污染控制问题。在仅给定这些问题的少量训练实例情况下，无需领域知识，该方法构建的并行算法组合在所有三类问题上均展现出优于现有方法的泛化性能，尽管现有方法使用了领域特定的实例生成器。