Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of “training” problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup–delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.

中文翻译：

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通过协同进化的少镜头并行算法组合构建

泛化，即解决在系统设计和开发阶段不可用的问题实例的能力，是智能系统的关键目标。实现良好泛化的典型方法是从大量数据中学习模型。在启发式搜索的上下文中，这种范式可以实现为基于一组“训练”问题实例配置并行算法组合 (PAP) 的参数，这通常称为 PAP 构造。然而，与传统的机器学习相比，PAP 的构建往往缺乏训练实例，并且获得的 PAP 可能无法很好地泛化。本文提出了一种新颖的竞争协同进化方案，称为参数化搜索协同进化（CEPS），作为应对这一挑战的补救措施。通过共同进化配置种群和实例种群，CEPS 能够以很少的训练实例获得可泛化的 PAP。本文分析展示了 CEPS 在提高泛化方面的优势。分别针对旅行商问题 (TSP) 和具有同时取货-交货和时间窗口的车辆路径问题 (VRPSPDTW) 提出了两种具体算法，即 CEPS-TSP 和 CEPS-VRPSPDTW。实验结果表明，CEPS 导致了更好的泛化，甚至设法为某些实例找到了新的最著名的解决方案。分别针对旅行商问题 (TSP) 和具有同时取货-交货和时间窗口 (VRPSPDTW) 的车辆路径问题。实验结果表明，CEPS 导致了更好的泛化，甚至设法为某些实例找到了新的最著名的解决方案。分别针对旅行商问题 (TSP) 和具有同时取货-交货和时间窗口 (VRPSPDTW) 的车辆路径问题。实验结果表明，CEPS 导致了更好的泛化，甚至设法为某些实例找到了新的最著名的解决方案。