In model-based evolutionary algorithms (MBEAs) the variation operators are guided by the
use of a model that conveys problem-specific information so as to increase the
chances that combining the currently available solutions leads to improved
solutions. Such models can be constructed beforehand for a specific problem, or
they can be learnt during the optimization process.
Replacing traditional crossover and mutation operators by building
and using models enables the use of machine learning techniques for automatic
discovery of problem regularities and subsequent exploitation of these
regularities, thereby enabling the design of optimization techniques that can
automatically adapt to a given problem. This is an especially useful feature
when considering optimization in a black-box setting. The use of models can
furthermore also have major implications for grey-box settings where not
everything about the problem is considered to be unknown a priori.

Well-known types of MBEAs are Estimation-of-Distribution Algorithms (EDAs)
where probabilistic models of promising solutions are built and samples are
subsequently drawn from these models to generate new solutions.
A more recent class of MBEAs is the family of Optimal Mixing EAs such
as the Linkage Tree GA and, more generally, various GOMEA variants.
The tutorial will mainly focus on the latter types of MBEAs.