  The analysis of vast amounts of data constitutes a major challenge in modern
high energy physics experiments. Machine learning (ML) methods, typically
trained on simulated data, are often employed to facilitate this task. Several
choices need to be made by the user when training the ML algorithm. In addition
to deciding which ML algorithm to use and choosing suitable observables as
inputs, users typically need to choose among a plethora of algorithm-specific
parameters. We refer to parameters that need to be chosen by the user as
hyperparameters. These are to be distinguished from parameters that the ML
algorithm learns autonomously during the training, without intervention by the
user. The choice of hyperparameters is conventionally done manually by the user
and often has a significant impact on the performance of the ML algorithm. In
this paper, we explore two evolutionary algorithms: particle swarm optimization
(PSO) and genetic algorithm (GA), for the purposes of performing the choice of
optimal hyperparameter values in an autonomous manner. Both of these algorithms
will be tested on different datasets and compared to alternative methods.
