  This paper presents the construction of a particle filter, which incorporates
elements inspired by genetic algorithms, in order to achieve accelerated
adaptation of the estimated posterior distribution to changes in model
parameters. Specifically, the filter is designed for the situation where the
subsequent data in online sequential filtering does not match the model
posterior filtered based on data up to a current point in time. The examples
considered encompass parameter regime shifts and stochastic volatility. The
filter adapts to regime shifts extremely rapidly and delivers a clear heuristic
for distinguishing between regime shifts and stochastic volatility, even though
the model dynamics assumed by the filter exhibit neither of those features.
