  xMLC is the second book of this `Machine Learning Tools in Fluid Mechanics'
Series and focuses on Machine Learning Control (MLC). The objectives of this
book are two-fold: First, provide an introduction to MLC for students,
researchers, and newcomers on the field; and second, share an open-source code,
xMLC, to automatically learn open- and closed-loop control laws directly in the
plant with only a few executable commands. This presented MLC algorithm is
based on genetic programming and highlights the learning principles
(exploration and exploitation). The need of balance between these two
principles is illustrated with an extensive parametric study where the
explorative and exploitative forces are gradually integrated in the
optimization process. The provided software xMLC is an implementation of MLC.
It builds on OpenMLC (Duriez et al., 2017) but replaces tree-based genetic
programming but the linear genetic programming framework (Brameier and Banzhaf,
2006). The latter representation is preferred for its easier implementation of
multiple-input multiple-output control laws and of the genetic operators
(mutation and crossover). The handling of the software is facilitated by a step
by step guide that shall help new practitioners to use the code within few
minutes. We also provide detailed advice in using the code for other solvers
and for experiments. The code is open-source and a GitHub version is available
for future updates, options and add-ons.

Fluid Dynamics