  DNA samples crime cases analysed in forensic genetics, frequently contain DNA
from multiple contributors. These occur as convolutions of the DNA profiles of
the individual contributors to the DNA sample. Thus, in cases where one or more
of the contributors were unknown, an objective of interest would be the
separation, often called deconvolution, of these unknown profiles. In order to
obtain deconvolutions of the unknown DNA profiles, we introduced a multiple
population evolutionary algorithm (MEA). We allowed the mutation operator of
the MEA to utilise that the fitness is based on a probabilistic model and guide
it by using the deviations between the observed and the expected value for
every element of the encoded individual. This guided mutation operator (GM) was
designed such that the larger the deviation the higher probability of mutation.
Furthermore, the GM was inhomogeneous in time, decreasing to a specified lower
bound as the number of iterations increased. We analysed 102 two-person DNA
mixture samples in varying mixture proportions. The samples were quantified
using two different DNA prep. kits: (1) Illumina ForenSeq Panel B (30 samples),
and (2) Applied Biosystems Precision ID Globalfiler NGS STR panel (72 samples).
The DNA mixtures were deconvoluted by the MEA and compared to the true DNA
profiles of the sample. We analysed three scenarios where we assumed: (1) the
DNA profile of the major contributor was unknown, (2) DNA profile of the minor
was unknown, and (3) both DNA profiles were unknown. Furthermore, we conducted
a series of sensitivity experiments on the ForenSeq panel by varying the
sub-population size, comparing a completely random homogeneous mutation
operator to the guided operator with varying mutation decay rates, and allowing
for hill-climbing of the parent population.
