Probabilistic model-building genetic algorithms

Probabilistic model-building genetic algorithms (PMBGAs), also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs), guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Building and sampling probabilistic models of promising solutions enables the use of machine learning techniques for automated discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. However, EDAs are not only optimization techniques; besides the optimum or its approximation, EDAs provide practitioners with a series of probabilistic models that reveal a lot of information about the problem being solved. This information can in turn be used to design problem-specific neighborhood operators for local search, to bias future runs of EDAs on a similar problem, or to create an efficient computational model of the problem. The tutorial Probabilistic Model-Building GAs provides an introduction to PMBGAs with an overview of major research directions in this area.