An information geometry perspective on estimation of distribution algorithms

Estimation of Distribution Algorithms are a recent new meta-heuristic used in Genetics-Based Machine Learning to solve combinatorial and continuous optimization problems. One of the distinctive features of this family of algorithms is that the search for the optimum is performed within a candidate space of probability distributions associated to the problem rather than over the population of possible solutions. A framework based on Information Geometry [3] is applied in this paper to propose a geometrical interpretation of the different operators used in EDAs and provide a better understanding of the underlying behavior of this family of algorithms from a novel point of view. The analysis carried out and the simple examples introduced show the importance of the boundary of the statistical model w.r.t. the distributions and EDA may converge to.