  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.
