Information geometry in evolutionary computation
Luigi Malagò, Tobias Glasmachers · 2014
In recent years there have been independent developments in multiple branches of Evolutionary Computation (EC) that interpret population-based and model-based search algorithms in terms of information geometric concepts. This trend has resulted in the development of novel algorithms and in improved understanding of existing ones. This tutorial aims at making this new line of research accessible to a broader range of researchers. A statistical model, identified by a parametric family of distributions, is equipped with an intrinsic (Riemannian) geometry, the so-called information geometry. From this perspective, a statistical model is a manifold of distributions where the inner product is given by the Fisher information metric. Any evolutionary algorithm that implicitly or explicitly evolves the parameters of a search distribution defines a dynamic over the manifold. Taking into account the Riemannian geometry of the new search space given by the search distributions allows for the description and analysis of evolutionary operators in a new light.