  Sampling from a multimodal distribution is a fundamental and challenging
problem in computational science and statistics. Among various approaches
proposed for this task, one popular method is Annealed Importance Sampling
(AIS). In this paper, we propose an ensemble-based version of AIS by combining
it with population-based Monte Carlo methods to improve its efficiency. By
keeping track of an ensemble instead of a single particle along some
continuation path between the starting distribution and the target
distribution, we take advantage of the interaction within the ensemble to
encourage the exploration of undiscovered modes. Specifically, our main idea is
to utilize either the snooker algorithm or the genetic algorithm used in
Evolutionary Monte Carlo. We discuss how the proposed algorithm can be
implemented and derive a partial differential equation governing the evolution
of the ensemble under the continuous time and mean-field limit. We also test
the efficiency of the proposed algorithm on various continuous and discrete
distributions.
