Many software systems operate in environments of change and uncertainty. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or removal of adaptation tactics. Instead, changes in a self-adaptive system often require a human planner to redo an expensive planning process to allow the system to continue satisfying its quality requirements under different conditions; automated techniques must replan from scratch. We propose to address this problem by reusing prior planning knowledge to adapt to unexpected situations. We present a planner based on genetic programming that reuses existing plans and evaluate this planner on two case-study systems: a cloud-based web server and a team of autonomous aircraft. While reusing material in genetic algorithms has been recently applied successfully in the area of automated program repair, we find that naively reusing existing plans for self- * planning can actually result in a utility loss. Furthermore, we propose a series of techniques to lower the costs of reuse, allowing genetic techniques to leverage existing information to improve utility when replanning for unexpected changes, and we find that coarsely shaped search-spaces present profitable opportunities for reuse.

中文翻译：

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信息重用和随机搜索

许多软件系统在变化和不确定的环境中运行。自适应技术允许这些系统自动响应环境变化，但它们不处理自适应系统本身的变化，例如添加或删除适应策略。相反，自适应系统的变化通常需要人类计划人员重新进行昂贵的计划过程，以使系统在不同条件下继续满足其质量要求；自动化技术必须从头开始重新规划。我们建议通过重用先前的规划知识来适应意外情况来解决这个问题。我们提出了一个基于遗传编程的规划器，它重用现有的计划，并在两个案例研究系统上评估这个规划器：一个基于云的 Web 服务器和一个自主飞机团队。*规划实际上会导致公用事业损失。此外，我们提出了一系列降低重用成本的技术，允许遗传技术在重新规划意外变化时利用现有信息提高效用，我们发现粗略形状的搜索空间为重用提供了有利可图的机会。