Kernels of Mallows Models for Solving Permutation-based Problems
Josu Ceberio, Alexander Mendiburu, Jose Antonio Lozano · 2015
Recently, distance-based exponential probability models, such as Mallows and Generalized Mallows, have demonstrated their validity in the context of estimation of distribution algorithms (EDAs) for solving permutation problems. However, despite their successful performance, these models are unimodal, and therefore, they are not flexible enough to accurately model populations with solutions that are very sparse with regard to the distance metric considered under the model.