A mathematical analysis of EDAs with distance-based exponential models

Abstract Estimation of Distribution Algorithms have been successfully used to solve permutation-based Combinatorial Optimization Problems. In this case, the algorithms use probabilistic models specifically designed for codifying probability distributions over permutation spaces. One class these are distance-based exponential models, and one example is Mallows model. spite its practical success, theoretical analysis Problems has not developed as extensively it binary problems. With motivation, paper presents a first mathematical convergence behavior based on models. The model removes randomness algorithm in order associate dynamical system it. Several scenarios increasing complexity with different fitness functions initial analyzed. obtained results show: a) strong dependence final population, b) possibility converge non-degenerate even very simple scenarios, which reported before literature.