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.