  We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms.
In these algorithms, a set of ``walkers'' or ``particles'' is used as a
representation of a high-dimensional vector. The computation is carried out by
a random walk and split/deletion of these objects. The algorithms are developed
in various fields in physics and statistical sciences and called by lots of
different terms -- ``quantum Monte Carlo'', ``transfer-matrix Monte Carlo'',
``Monte Carlo filter (particle filter)'',``sequential Monte Carlo'' and
``PERM'' etc. Here we discuss them in a coherent framework. We also touch on
related algorithms -- genetic algorithms and annealed importance sampling.
