  Spiking neural networks (SNNs) are gaining increasing attention as potential
computationally efficient alternatives to traditional artificial neural
networks(ANNs). However, the unique information propagation mechanisms and the
complexity of SNN neuron models pose challenges for adopting traditional
methods developed for ANNs to SNNs. These challenges include both weight
learning and architecture design. While surrogate gradient learning has shown
some success in addressing the former challenge, the latter remains relatively
unexplored. Recently, a novel paradigm utilizing evolutionary computation
methods has emerged to tackle these challenges. This approach has resulted in
the development of a variety of energy-efficient and high-performance SNNs
across a wide range of machine learning benchmarks. In this paper, we present a
survey of these works and initiate discussions on potential challenges ahead.
