  Is the lottery ticket phenomenon an idiosyncrasy of gradient-based training
or does it generalize to evolutionary optimization? In this paper we establish
the existence of highly sparse trainable initializations for evolution
strategies (ES) and characterize qualitative differences compared to gradient
descent (GD)-based sparse training. We introduce a novel signal-to-noise
iterative pruning procedure, which incorporates loss curvature information into
the network pruning step. This can enable the discovery of even sparser
trainable network initializations when using black-box evolution as compared to
GD-based optimization. Furthermore, we find that these initializations encode
an inductive bias, which transfers across different ES, related tasks and even
to GD-based training. Finally, we compare the local optima resulting from the
different optimization paradigms and sparsity levels. In contrast to GD, ES
explore diverse and flat local optima and do not preserve linear mode
connectivity across sparsity levels and independent runs. The results highlight
qualitative differences between evolution and gradient-based learning dynamics,
which can be uncovered by the study of iterative pruning procedures.
