Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive process based on experience, experimentation, and reasoning by metaphor. More recently, there has been interest in automating the process of algorithm configuration. In this article, we identify shared state as an inhibitor of progress for such automation. To solve this problem, we introduce the Automated Open-Closed Principle (AOCP), which stipulates design requirements for unintrusive reuse of algorithm frameworks and automated assembly of algorithms from an extensible palette of components. We demonstrate how the AOCP enables a greater degree of automation than previously possible via an example implementation.

中文翻译：

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将“开闭原则”扩展到自动算法配置

元启发式算法是一类有效且多样化的优化算法：一种为其他难以处理的问题获得质量可接受的解决方案的方法。对于给定问题，元启发式的选择、构建和配置在历史上一直是基于经验、实验和隐喻推理的手动密集过程。最近，人们对算法配置过程的自动化产生了兴趣。在本文中，我们将共享状态确定为此类自动化进展的阻碍因素。为了解决这个问题，我们引入了自动开闭原则 (AOCP)，它规定了算法框架的非侵入式重用和从可扩展的组件调色板自动组装算法的设计要求。