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.
翻译:在当今的数字世界中,我们面临着由众多大型云型应用产生和操纵的数据和模型的爆炸。在这样的环境下,现有的转移进化优化框架同时满足两个重要的质量属性,即:(1) 相对于越来越多的源任务进行缩放,(2) 在线学习对相关源任务扩大的灵活度,(2) 与目标任务任务的任务范围扩大相比,在线学习灵活度。满足这些属性将便利在任务范围大的情况下实际部署转让优化,同时遏制负面转移的威胁。虽然现有算法的应用仅限于数十项源任务,但在本文件中,我们采取飞跃式的提升框架,使任务数量的规模扩大有两个以上层次的精度;也就是说,我们有效地处理超过1000个源任务范围的情景;我们设计了一个全新的转移演化优化框架,包括两个共同变化的物种,用于在源知识空间和目标问题的解决方案搜索空间进行联合演进。特别是,共同演进使得所学知识在飞翔上得以协调,而不是在目标优化任务中加快趋同,我们在两个目标任务规模的深度规模上,我们进行了一个连续、连续、连续、连续的系列的实验,一个相关任务规模的实验,显示我们一个连续、连续、连续的系列的相关任务规模的系列的实验,显示一个相关的实验,一个相关的系列的系列的实验,只是一个连续的系列的实验,一个相关的实验。