of modules and topologies together improves even more, and the capstone architecture turns out to be the best (at 88%). The results thus demonstrate three general points: evolutionary architecture search can make a large difference in performance of deep learning networks; MTL can improve performance of deep learning tasks; and putting these together results in a particularly powerful approach. In the future it can be applied to various problems in vision, language, and control, and in particular to domains with multimodal inputs and outputs. The rest of the paper is organized as follows: In Section \[sec:background\], previous work on deep MTL and neural architecture search are summarized. In Section \[sec:algorithms\], the key contribution of this paper, novel evolutionary algorithms for architecture search of multitask networks are described. Finally, in Section \[sec:experiments\] and Section \[sec:discussion\] experimental results on the Omniglot domain are presented and analyzed.

Background and Related Work {#sec:background}
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Before introducing methods for combining them in Section \[sec:algorithms\], this section reviews deep MTL and neural architecture search.

![\[fig:soft\_order\] Example soft ordering network with three shared layers. Soft ordering learns how to use the same layers in different locations by learning a tensor $S$ of task-specific scaling parameters. $S$ is 