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.

翻译：在法医遗传学中分析的DNA样本犯罪案例中,往往含有多个贡献者的DNA,这些案例是作为DNA样本个体贡献者的DNA剖面图的变异而发生的,因此,在一个或多个贡献者对DNA样本的DNA剖面图不明的情况下,一个感兴趣的目标是将这些未知特征的变异(通常称为变异)分开;为了获得未知DNA剖面图的变异,我们引入了多种人口演化算法(MEA)。我们允许MEA的突变操作者利用一种概率模型来测定是否适合,并使用观察到的DNA剖面图个人每个元素的DNA剖面图与预期值之间的偏差来指导它。这个受引导的突变操作者(GM)的设计目标是,使突变概率越大,变概率越高。 此外,GMMER的变异样图越多,我们分析了102份DNA混合物样本,用两种不同的DNA剖面样本进行了量化:(1) Ilurumimia Forsen-Se B(30) 样本, 和 应用DNA剖面图解的DNA剖面图比比(我们做了DNA的DNA剖面图的变) 。