Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models

In this paper, evolutionary algorithms based on probabilistic models (EAPMs) have been recognized as a new computing paradigm in evolutionary computation. There is no traditional crossover or mutation in EAPMs. Instead, they explicitly extract global statistical information from their previous search and build a probability distribution model of promising solutions, based on the extracted information. New solutions are then sampled from the model thus built to replace old solutions. Instances of EAPMs include Population-Based Incremental Learning, the Univariate Marginal Distribution Algorithm (UMDA), Mutual Information Maximization for Input Clustering, the Factorized Distribution Algorithm, the Bayesian Optimization Algorithm, the Learnable Evolution Model and Estimation of Bayesian Networks Algorithms, to name a few. EAPMs have been successfully applied for solving many optimization and search problems.