  This study proposes a data condensation method for multivariate kernel
density estimation by genetic algorithm. First, our proposed algorithm
generates multiple subsamples of a given size with replacement from the
original sample. The subsamples and their constituting data points are regarded
as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of
genetic algorithm. Second, each pair of subsamples breeds two new subsamples,
where each data point faces either $\it{crossover}$, $\it{mutation}$, or
$\it{reproduction}$ with a certain probability. The dominant subsamples in
terms of fitness values are inherited by the next generation. This process is
repeated generation by generation and brings the sparse representation of
kernel density estimator in its completion. We confirmed from simulation
studies that the resulting estimator can perform better than other well-known
density estimators.
