可扩展的转移进化优化：应对大任务挑战
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
摘要：
在当今的数字世界中，我们面临着由众多基于云的大规模应用程序产生和操纵的数据和模型的爆炸式增长。在这样的设置下，现有的转移进化优化框架努力同时满足两个重要的质量属性，即（1）针对数量不断增长的源任务的可扩展性和（2）针对相关源到感兴趣的目标任务的稀疏性的在线学习敏捷性。满足这些属性将有助于将传输优化实际部署到具有大任务实例的场景，同时抑制负传输的威胁。虽然现有算法的应用仅限于数十个源任务，但在本文中，我们在任务数量上实现了两个以上数量级的扩展，即，我们有效地处理超过1000个源任务实例的场景。我们设计了一种新的转移进化优化框架，包括两个共同进化的物种在源知识空间和目标问题的解决方案的搜索空间中的联合进化。特别是，协同进化使学习到的知识能够在运行中进行编排，加快目标优化任务的收敛。我们已经进行了一系列广泛的实验，在一组实际动机的离散和连续优化的例子，包括大量的源任务实例，其中只有一小部分表示源目标相关性。实验结果表明，我们提出的框架不仅可以有效地扩展与不断增长的源任务的数量，但也有效地捕获相关的知识对稀疏的相关来源，满足可扩展性和在线学习敏捷性的两个显着特点。
英文摘要：
In today's digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing transfer evolutionary optimization frameworks grapple with simultaneously satisfying two important quality attributes, namely (1) scalability against a growing number of source tasks and (2) online learning agility against sparsity of relevant sources to the target task of interest. Satisfying these attributes shall facilitate practical deployment of transfer optimization to scenarios with big task-instances, while curbing the threat of negative transfer. While applications of existing algorithms are limited to tens of source tasks, in this paper, we take a quantum leap forward in enabling more than two orders of magnitude scale-up in the number of tasks; i.e., we efficiently handle scenarios beyond 1000 source task-instances. We devise a novel transfer evolutionary optimization framework comprising two co-evolving species for joint evolutions in the space of source knowledge and in the search space of solutions to the target problem. In particular, co-evolution enables the learned knowledge to be orchestrated on the fly, expediting convergence in the target optimization task. We have conducted an extensive series of experiments across a set of practically motivated discrete and continuous optimization examples comprising a large number of source task-instances, of which only a small fraction indicate source-target relatedness. The experimental results show that not only does our proposed framework scale efficiently with a growing number of source tasks but is also effective in capturing relevant knowledge against sparsity of related sources, fulfilling the two salient features of scalability and online learning agility.
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